Automatic grammar detection and correction

ABSTRACT

Systems and processes for operating an intelligent automated assistant are provided. In one example process a set of words including a grammatical error is received. The process can generate, using a neural network based on the set of words including the grammatical error and a reference set of words, a transformed set of words and further determine, based on the set of words including the grammatical error and the reference set of words, a reconstructed reference set of words. The process can also determine, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct and provide an indication of whether the transformed set of words is grammatically correct to the neural network.

FIELD

This relates generally to intelligent automated assistants and, more specifically, to intelligent detection and correction of grammatical errors within user input text.

BACKGROUND

Intelligent automated assistants (or digital assistants) can provide a beneficial interface between human users and electronic devices. Such assistants can allow users to interact with devices or systems using natural language in spoken and/or text forms. For example, a user can provide a speech input containing a user request to a digital assistant operating on an electronic device. The digital assistant can interpret the user's intent from the speech input and operationalize the user's intent into tasks. The tasks can then be performed by executing one or more services of the electronic device, and a relevant output responsive to the user request can be returned to the user.

In some circumstances, intelligent automated assistants can perform tasks automatically without receiving a request from a user. For example, a user may enter text into an application in order to perform a task, such as sending a text message or email to a friend. While entering the text, the user may enter a set of words that contains a grammatical error. In such cases, the intelligent automated assistant may recognize the grammatical error and provide the user with a notification of the grammatical error. However, detection of grammatical errors may be limited to a few specific types of common grammatical errors and thus may ignore other errors that the user makes. As an example, an intelligent automated assistant may not recognize a grammatical error when a word entered by the user is spelled correctly but contextually incorrect. As another example, the detection of grammatical errors may be limited to errors that the user has previously made and corrected a predetermined number of times.

SUMMARY

Example methods are disclosed herein. An example method includes, at an electronic device having one or more processors, receiving a set of words including a grammatical error; generating, using a neural network based on the set of words including the grammatical error and a reference set of words, a transformed set of words; determining, based on the set of words including the grammatical error and the reference set of words, a reconstructed reference set of words; determining, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct; and providing an indication of whether the transformed set of words is grammatically correct to the neural network.

Example non-transitory computer-readable media are disclosed herein. An example non-transitory computer-readable storage medium stores one or more programs. The one or more programs comprise instructions, which when executed by one or more processors of an electronic device, cause the electronic device to receive a set of words including a grammatical error; generate, using a neural network based on the set of words including the grammatical error and a reference set of words, a transformed set of words; determine, based on the set of words including the grammatical error and the reference set of words, a reconstructed reference set of words; determine, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct; and provide an indication of whether the transformed set of words is grammatically correct to the neural network.

Example electronic devices are disclosed herein. An example electronic device comprises one or more processors; a memory; and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for receiving a set of words including a grammatical error; generating, using a neural network based on the set of words including the grammatical error and a reference set of words, a transformed set of words; determining, based on the set of words including the grammatical error and the reference set of words, a reconstructed reference set of words; determining, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct; and providing an indication of whether the transformed set of words is grammatically correct to the neural network.

An example electronic device comprises means for receiving a set of words including a grammatical error; generating, using a neural network based on the set of words including the grammatical error and a reference set of words, a transformed set of words; determining, based on the set of words including the grammatical error and the reference set of words, a reconstructed reference set of words; determining, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct; and providing an indication of whether the transformed set of words is grammatically correct to the neural network.

Techniques for intelligent detection and correction of grammatical errors are desirable. In particular, it a model or network for intelligent detection and correction of grammatical errors can be generated using unsupervised training. Performing unsupervised training of a network or model in this way reduces or eliminates the need for an impractically-large data set including all conceivable grammatical errors required to perform a supervised training. A network trained in this way can then be incorporated into a user device or a digital assistant to provide more intelligent and efficient detection and correction of grammatical errors in some examples. In other examples the trained network can be used to create a data set including incorrect and corrected sets of words which can be further used to train other networks to detect and correct grammatical errors. Thus an improved model or network may be created and implemented in a system to correct a wider variety of grammatical errors in a more efficient manner.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system and environment for implementing a digital assistant, according to various examples.

FIG. 2A is a block diagram illustrating a portable multifunction device implementing the client-side portion of a digital assistant, according to various examples.

FIG. 2B is a block diagram illustrating exemplary components for event handling, according to various examples.

FIG. 3 illustrates a portable multifunction device implementing the client-side portion of a digital assistant, according to various examples.

FIG. 4 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface, according to various examples.

FIG. 5A illustrates an exemplary user interface for a menu of applications on a portable multifunction device, according to various examples.

FIG. 5B illustrates an exemplary user interface for a multifunction device with a touch-sensitive surface that is separate from the display, according to various examples.

FIG. 6A illustrates a personal electronic device, according to various examples.

FIG. 6B is a block diagram illustrating a personal electronic device, according to various examples.

FIG. 7A is a block diagram illustrating a digital assistant system or a server portion thereof, according to various examples.

FIG. 7B illustrates the functions of the digital assistant shown in FIG. 7A, according to various examples.

FIG. 7C illustrates a portion of an ontology, according to various examples.

FIG. 8 illustrates a block diagram of a digital assistant for generating a grammatical error correction model, according to various examples.

FIG. 9 illustrates a block diagram of a neural network for generating grammatically correct sets of words, according to various examples.

FIG. 10 illustrates a block diagram of a reference set of words reconstructor, according to various examples.

FIG. 11 illustrates a user interface for receiving user input text, according to various examples.

FIG. 12 illustrates a block diagram of a digital assistant for providing a corrected set of words, according to various examples.

FIGS. 13A-13B illustrate user interfaces for providing candidate words to the user, according to various examples.

FIGS. 14A-14D illustrate an exemplary process for operating a digital assistant to provide grammatical error detection and correction, according to various examples.

DETAILED DESCRIPTION

In the following description of examples, reference is made to the accompanying drawings in which are shown by way of illustration specific examples that can be practiced. It is to be understood that other examples can be used and structural changes can be made without departing from the scope of the various examples.

Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first input could be termed a second input, and, similarly, a second input could be termed a first input, without departing from the scope of the various described examples. The first input and the second input are both inputs and, in some cases, are separate and different inputs.

The terminology used in the description of the various described examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various described examples and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.

1. System and Environment

FIG. 1 illustrates a block diagram of system 100 according to various examples. In some examples, system 100 implements a digital assistant. The terms “digital assistant,” “virtual assistant,” “intelligent automated assistant,” or “automatic digital assistant” refer to any information processing system that interprets natural language input in spoken and/or textual form to infer user intent, and performs actions based on the inferred user intent. For example, to act on an inferred user intent, the system performs one or more of the following: identifying a task flow with steps and parameters designed to accomplish the inferred user intent, inputting specific requirements from the inferred user intent into the task flow; executing the task flow by invoking programs, methods, services, APIs, or the like; and generating output responses to the user in an audible (e.g., speech) and/or visual form.

Specifically, a digital assistant is capable of accepting a user request at least partially in the form of a natural language command, request, statement, narrative, and/or inquiry. Typically, the user request seeks either an informational answer or performance of a task by the digital assistant. A satisfactory response to the user request includes a provision of the requested informational answer, a performance of the requested task, or a combination of the two. For example, a user asks the digital assistant a question, such as “Where am I right now?” Based on the user's current location, the digital assistant answers, “You are in Central Park near the west gate.” The user also requests the performance of a task, for example, “Please invite my friends to my girlfriend's birthday party next week.” In response, the digital assistant can acknowledge the request by saying “Yes, right away,” and then send a suitable calendar invite on behalf of the user to each of the user's friends listed in the user's electronic address book. During performance of a requested task, the digital assistant sometimes interacts with the user in a continuous dialogue involving multiple exchanges of information over an extended period of time. There are numerous other ways of interacting with a digital assistant to request information or performance of various tasks. In addition to providing verbal responses and taking programmed actions, the digital assistant also provides responses in other visual or audio forms, e.g., as text, alerts, music, videos, animations, etc.

As shown in FIG. 1, in some examples, a digital assistant is implemented according to a client-server model. The digital assistant includes client-side portion 102 (hereafter “DA client 102”) executed on user device 104 and server-side portion 106 (hereafter “DA server 106”) executed on server system 108. DA client 102 communicates with DA server 106 through one or more networks 110. DA client 102 provides client-side functionalities such as user-facing input and output processing and communication with DA server 106. DA server 106 provides server-side functionalities for any number of DA clients 102 each residing on a respective user device 104.

In some examples, DA server 106 includes client-facing I/O interface 112, one or more processing modules 114, data and models 116, and I/O interface to external services 118. The client-facing I/O interface 112 facilitates the client-facing input and output processing for DA server 106. One or more processing modules 114 utilize data and models 116 to process speech input and determine the user's intent based on natural language input. Further, one or more processing modules 114 perform task execution based on inferred user intent. In some examples, DA server 106 communicates with external services 120 through network(s) 110 for task completion or information acquisition. I/O interface to external services 118 facilitates such communications.

User device 104 can be any suitable electronic device. In some examples, user device 104 is a portable multifunctional device (e.g., device 200, described below with reference to FIG. 2A), a multifunctional device (e.g., device 400, described below with reference to FIG. 4), or a personal electronic device (e.g., device 600, described below with reference to FIG. 6A-B.) A portable multifunctional device is, for example, a mobile telephone that also contains other functions, such as PDA and/or music player functions. Specific examples of portable multifunction devices include the Apple Watch®, iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other examples of portable multifunction devices include, without limitation, earphones/headphones, speakers, and laptop or tablet computers. Further, in some examples, user device 104 is a non-portable multifunctional device. In particular, user device 104 is a desktop computer, a game console, a speaker, a television, or a television set-top box. In some examples, user device 104 includes a touch-sensitive surface (e.g., touch screen displays and/or touchpads). Further, user device 104 optionally includes one or more other physical user-interface devices, such as a physical keyboard, a mouse, and/or a joystick. Various examples of electronic devices, such as multifunctional devices, are described below in greater detail.

Examples of communication network(s) 110 include local area networks (LAN) and wide area networks (WAN), e.g., the Internet. Communication network(s) 110 is implemented using any known network protocol, including various wired or wireless protocols, such as, for example, Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or any other suitable communication protocol.

Server system 108 is implemented on one or more standalone data processing apparatus or a distributed network of computers. In some examples, server system 108 also employs various virtual devices and/or services of third-party service providers (e.g., third-party cloud service providers) to provide the underlying computing resources and/or infrastructure resources of server system 108.

In some examples, user device 104 communicates with DA server 106 via second user device 122. Second user device 122 is similar or identical to user device 104. For example, second user device 122 is similar to devices 200, 400, or 600 described below with reference to FIGS. 2A, 4, and 6A-B. User device 104 is configured to communicatively couple to second user device 122 via a direct communication connection, such as Bluetooth, NFC, BTLE, or the like, or via a wired or wireless network, such as a local Wi-Fi network. In some examples, second user device 122 is configured to act as a proxy between user device 104 and DA server 106. For example, DA client 102 of user device 104 is configured to transmit information (e.g., a user request received at user device 104) to DA server 106 via second user device 122. DA server 106 processes the information and returns relevant data (e.g., data content responsive to the user request) to user device 104 via second user device 122.

In some examples, user device 104 is configured to communicate abbreviated requests for data to second user device 122 to reduce the amount of information transmitted from user device 104. Second user device 122 is configured to determine supplemental information to add to the abbreviated request to generate a complete request to transmit to DA server 106. This system architecture can advantageously allow user device 104 having limited communication capabilities and/or limited battery power (e.g., a watch or a similar compact electronic device) to access services provided by DA server 106 by using second user device 122, having greater communication capabilities and/or battery power (e.g., a mobile phone, laptop computer, tablet computer, or the like), as a proxy to DA server 106. While only two user devices 104 and 122 are shown in FIG. 1, it should be appreciated that system 100, in some examples, includes any number and type of user devices configured in this proxy configuration to communicate with DA server system 106.

Although the digital assistant shown in FIG. 1 includes both a client-side portion (e.g., DA client 102) and a server-side portion (e.g., DA server 106), in some examples, the functions of a digital assistant are implemented as a standalone application installed on a user device. In addition, the divisions of functionalities between the client and server portions of the digital assistant can vary in different implementations. For instance, in some examples, the DA client is a thin-client that provides only user-facing input and output processing functions, and delegates all other functionalities of the digital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices for implementing the client-side portion of a digital assistant. FIG. 2A is a block diagram illustrating portable multifunction device 200 with touch-sensitive display system 212 in accordance with some embodiments. Touch-sensitive display 212 is sometimes called a “touch screen” for convenience and is sometimes known as or called a “touch-sensitive display system.” Device 200 includes memory 202 (which optionally includes one or more computer-readable storage mediums), memory controller 222, one or more processing units (CPUs) 220, peripherals interface 218, RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, input/output (I/O) subsystem 206, other input control devices 216, and external port 224. Device 200 optionally includes one or more optical sensors 264. Device 200 optionally includes one or more contact intensity sensors 265 for detecting intensity of contacts on device 200 (e.g., a touch-sensitive surface such as touch-sensitive display system 212 of device 200). Device 200 optionally includes one or more tactile output generators 267 for generating tactile outputs on device 200 (e.g., generating tactile outputs on a touch-sensitive surface such as touch-sensitive display system 212 of device 200 or touchpad 455 of device 400). These components optionally communicate over one or more communication buses or signal lines 203.

As used in the specification and claims, the term “intensity” of a contact on a touch-sensitive surface refers to the force or pressure (force per unit area) of a contact (e.g., a finger contact) on the touch-sensitive surface, or to a substitute (proxy) for the force or pressure of a contact on the touch-sensitive surface. The intensity of a contact has a range of values that includes at least four distinct values and more typically includes hundreds of distinct values (e.g., at least 256). Intensity of a contact is, optionally, determined (or measured) using various approaches and various sensors or combinations of sensors. For example, one or more force sensors underneath or adjacent to the touch-sensitive surface are, optionally, used to measure force at various points on the touch-sensitive surface. In some implementations, force measurements from multiple force sensors are combined (e.g., a weighted average) to determine an estimated force of a contact. Similarly, a pressure-sensitive tip of a stylus is, optionally, used to determine a pressure of the stylus on the touch-sensitive surface. Alternatively, the size of the contact area detected on the touch-sensitive surface and/or changes thereto, the capacitance of the touch-sensitive surface proximate to the contact and/or changes thereto, and/or the resistance of the touch-sensitive surface proximate to the contact and/or changes thereto are, optionally, used as a substitute for the force or pressure of the contact on the touch-sensitive surface. In some implementations, the substitute measurements for contact force or pressure are used directly to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is described in units corresponding to the substitute measurements). In some implementations, the substitute measurements for contact force or pressure are converted to an estimated force or pressure, and the estimated force or pressure is used to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is a pressure threshold measured in units of pressure). Using the intensity of a contact as an attribute of a user input allows for user access to additional device functionality that may otherwise not be accessible by the user on a reduced-size device with limited real estate for displaying affordances (e.g., on a touch-sensitive display) and/or receiving user input (e.g., via a touch-sensitive display, a touch-sensitive surface, or a physical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output” refers to physical displacement of a device relative to a previous position of the device, physical displacement of a component (e.g., a touch-sensitive surface) of a device relative to another component (e.g., housing) of the device, or displacement of the component relative to a center of mass of the device that will be detected by a user with the user's sense of touch. For example, in situations where the device or the component of the device is in contact with a surface of a user that is sensitive to touch (e.g., a finger, palm, or other part of a user's hand), the tactile output generated by the physical displacement will be interpreted by the user as a tactile sensation corresponding to a perceived change in physical characteristics of the device or the component of the device. For example, movement of a touch-sensitive surface (e.g., a touch-sensitive display or trackpad) is, optionally, interpreted by the user as a “down click” or “up click” of a physical actuator button. In some cases, a user will feel a tactile sensation such as an “down click” or “up click” even when there is no movement of a physical actuator button associated with the touch-sensitive surface that is physically pressed (e.g., displaced) by the user's movements. As another example, movement of the touch-sensitive surface is, optionally, interpreted or sensed by the user as “roughness” of the touch-sensitive surface, even when there is no change in smoothness of the touch-sensitive surface. While such interpretations of touch by a user will be subject to the individualized sensory perceptions of the user, there are many sensory perceptions of touch that are common to a large majority of users. Thus, when a tactile output is described as corresponding to a particular sensory perception of a user (e.g., an “up click,” a “down click,” “roughness”), unless otherwise stated, the generated tactile output corresponds to physical displacement of the device or a component thereof that will generate the described sensory perception for a typical (or average) user.

It should be appreciated that device 200 is only one example of a portable multifunction device, and that device 200 optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. The various components shown in FIG. 2A are implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application-specific integrated circuits.

Memory 202 includes one or more computer-readable storage mediums. The computer-readable storage mediums are, for example, tangible and non-transitory. Memory 202 includes high-speed random access memory and also includes non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller 222 controls access to memory 202 by other components of device 200.

In some examples, a non-transitory computer-readable storage medium of memory 202 is used to store instructions (e.g., for performing aspects of processes described below) for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In other examples, the instructions (e.g., for performing aspects of the processes described below) are stored on a non-transitory computer-readable storage medium (not shown) of the server system 108 or are divided between the non-transitory computer-readable storage medium of memory 202 and the non-transitory computer-readable storage medium of server system 108.

Peripherals interface 218 is used to couple input and output peripherals of the device to CPU 220 and memory 202. The one or more processors 220 run or execute various software programs and/or sets of instructions stored in memory 202 to perform various functions for device 200 and to process data. In some embodiments, peripherals interface 218, CPU 220, and memory controller 222 are implemented on a single chip, such as chip 204. In some other embodiments, they are implemented on separate chips.

RF (radio frequency) circuitry 208 receives and sends RF signals, also called electromagnetic signals. RF circuitry 208 converts electrical signals to/from electromagnetic signals and communicates with communications networks and other communications devices via the electromagnetic signals. RF circuitry 208 optionally includes well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. RF circuitry 208 optionally communicates with networks, such as the Internet, also referred to as the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The RF circuitry 208 optionally includes well-known circuitry for detecting near field communication (NFC) fields, such as by a short-range communication radio. The wireless communication optionally uses any of a plurality of communications standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n, and/or IEEE 802.11ac), voice over Internet Protocol (VoP), Wi-MAX, a protocol for e mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document.

Audio circuitry 210, speaker 211, and microphone 213 provide an audio interface between a user and device 200. Audio circuitry 210 receives audio data from peripherals interface 218, converts the audio data to an electrical signal, and transmits the electrical signal to speaker 211. Speaker 211 converts the electrical signal to human-audible sound waves. Audio circuitry 210 also receives electrical signals converted by microphone 213 from sound waves. Audio circuitry 210 converts the electrical signal to audio data and transmits the audio data to peripherals interface 218 for processing. Audio data are retrieved from and/or transmitted to memory 202 and/or RF circuitry 208 by peripherals interface 218. In some embodiments, audio circuitry 210 also includes a headset jack (e.g., 312, FIG. 3). The headset jack provides an interface between audio circuitry 210 and removable audio input/output peripherals, such as output-only headphones or a headset with both output (e.g., a headphone for one or both ears) and input (e.g., a microphone).

I/O subsystem 206 couples input/output peripherals on device 200, such as touch screen 212 and other input control devices 216, to peripherals interface 218. I/O subsystem 206 optionally includes display controller 256, optical sensor controller 258, intensity sensor controller 259, haptic feedback controller 261, and one or more input controllers 260 for other input or control devices. The one or more input controllers 260 receive/send electrical signals from/to other input control devices 216. The other input control devices 216 optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controller(s) 260 are, optionally, coupled to any (or none) of the following: a keyboard, an infrared port, a USB port, and a pointer device such as a mouse. The one or more buttons (e.g., 308, FIG. 3) optionally include an up/down button for volume control of speaker 211 and/or microphone 213. The one or more buttons optionally include a push button (e.g., 306, FIG. 3).

A quick press of the push button disengages a lock of touch screen 212 or begin a process that uses gestures on the touch screen to unlock the device, as described in U.S. patent application Ser. No. 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g., 306) turns power to device 200 on or off. The user is able to customize a functionality of one or more of the buttons. Touch screen 212 is used to implement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 212 provides an input interface and an output interface between the device and a user. Display controller 256 receives and/or sends electrical signals from/to touch screen 212. Touch screen 212 displays visual output to the user. The visual output includes graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output correspond to user-interface objects.

Touch screen 212 has a touch-sensitive surface, sensor, or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen 212 and display controller 256 (along with any associated modules and/or sets of instructions in memory 202) detect contact (and any movement or breaking of the contact) on touch screen 212 and convert the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web pages, or images) that are displayed on touch screen 212. In an exemplary embodiment, a point of contact between touch screen 212 and the user corresponds to a finger of the user.

Touch screen 212 uses LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies may be used in other embodiments. Touch screen 212 and display controller 256 detect contact and any movement or breaking thereof using any of a plurality of touch sensing technologies now known or later developed, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch screen 212. In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that found in the iPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 212 is analogous to the multi-touch sensitive touchpads described in the following U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen 212 displays visual output from device 200, whereas touch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 212 is as described in the following applications: (1) U.S. patent application Ser. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2, 2006; (2) U.S. patent application Ser. No. 10/840,862, “Multipoint Touchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No. 10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30, 2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures For Touch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patent application Ser. No. 11/038,590, “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patent application Ser. No. 11/228,758, “Virtual Input Device Placement On A Touch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patent application Ser. No. 11/228,700, “Operation Of A Computer With A Touch Screen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser. No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No. 11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. All of these applications are incorporated by reference herein in their entirety.

Touch screen 212 has, for example, a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user makes contact with touch screen 212 using any suitable object or appendage, such as a stylus, a finger, and so forth. In some embodiments, the user interface is designed to work primarily with finger-based contacts and gestures, which can be less precise than stylus-based input due to the larger area of contact of a finger on the touch screen. In some embodiments, the device translates the rough finger-based input into a precise pointer/cursor position or command for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 200 includes a touchpad (not shown) for activating or deactivating particular functions. In some embodiments, the touchpad is a touch-sensitive area of the device that, unlike the touch screen, does not display visual output. The touchpad is a touch-sensitive surface that is separate from touch screen 212 or an extension of the touch-sensitive surface formed by the touch screen.

Device 200 also includes power system 262 for powering the various components. Power system 262 includes a power management system, one or more power sources (e.g., battery, alternating current (AC)), a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator (e.g., a light-emitting diode (LED)) and any other components associated with the generation, management and distribution of power in portable devices.

Device 200 also includes one or more optical sensors 264. FIG. 2A shows an optical sensor coupled to optical sensor controller 258 in I/O subsystem 206. Optical sensor 264 includes charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) phototransistors. Optical sensor 264 receives light from the environment, projected through one or more lenses, and converts the light to data representing an image. In conjunction with imaging module 243 (also called a camera module), optical sensor 264 captures still images or video. In some embodiments, an optical sensor is located on the back of device 200, opposite touch screen display 212 on the front of the device so that the touch screen display is used as a viewfinder for still and/or video image acquisition. In some embodiments, an optical sensor is located on the front of the device so that the user's image is obtained for video conferencing while the user views the other video conference participants on the touch screen display. In some embodiments, the position of optical sensor 264 can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a single optical sensor 264 is used along with the touch screen display for both video conferencing and still and/or video image acquisition.

Device 200 optionally also includes one or more contact intensity sensors 265. FIG. 2A shows a contact intensity sensor coupled to intensity sensor controller 259 in I/O subsystem 206. Contact intensity sensor 265 optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electric force sensors, piezoelectric force sensors, optical force sensors, capacitive touch-sensitive surfaces, or other intensity sensors (e.g., sensors used to measure the force (or pressure) of a contact on a touch-sensitive surface). Contact intensity sensor 265 receives contact intensity information (e.g., pressure information or a proxy for pressure information) from the environment. In some embodiments, at least one contact intensity sensor is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system 212). In some embodiments, at least one contact intensity sensor is located on the back of device 200, opposite touch screen display 212, which is located on the front of device 200.

Device 200 also includes one or more proximity sensors 266. FIG. 2A shows proximity sensor 266 coupled to peripherals interface 218. Alternately, proximity sensor 266 is coupled to input controller 260 in I/O subsystem 206. Proximity sensor 266 is performed as described in U.S. patent application Ser. No. 11/241,839, “Proximity Detector In Handheld Device”; Ser. No. 11/240,788, “Proximity Detector In Handheld Device”; Ser. No. 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; Ser. No. 11/586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and Ser. No. 11/638,251, “Methods And Systems For Automatic Configuration Of Peripherals,” which are hereby incorporated by reference in their entirety. In some embodiments, the proximity sensor turns off and disables touch screen 212 when the multifunction device is placed near the user's ear (e.g., when the user is making a phone call).

Device 200 optionally also includes one or more tactile output generators 267. FIG. 2A shows a tactile output generator coupled to haptic feedback controller 261 in I/O subsystem 206. Tactile output generator 267 optionally includes one or more electroacoustic devices such as speakers or other audio components and/or electromechanical devices that convert energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component that converts electrical signals into tactile outputs on the device). Contact intensity sensor 265 receives tactile feedback generation instructions from haptic feedback module 233 and generates tactile outputs on device 200 that are capable of being sensed by a user of device 200. In some embodiments, at least one tactile output generator is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system 212) and, optionally, generates a tactile output by moving the touch-sensitive surface vertically (e.g., in/out of a surface of device 200) or laterally (e.g., back and forth in the same plane as a surface of device 200). In some embodiments, at least one tactile output generator sensor is located on the back of device 200, opposite touch screen display 212, which is located on the front of device 200.

Device 200 also includes one or more accelerometers 268. FIG. 2A shows accelerometer 268 coupled to peripherals interface 218. Alternately, accelerometer 268 is coupled to an input controller 260 in I/O subsystem 206. Accelerometer 268 performs, for example, as described in U.S. Patent Publication No. 20050190059, “Acceleration-based Theft Detection System for Portable Electronic Devices,” and U.S. Patent Publication No. 20060017692, “Methods And Apparatuses For Operating A Portable Device Based On An Accelerometer,” both of which are incorporated by reference herein in their entirety. In some embodiments, information is displayed on the touch screen display in a portrait view or a landscape view based on an analysis of data received from the one or more accelerometers. Device 200 optionally includes, in addition to accelerometer(s) 268, a magnetometer (not shown) and a GPS (or GLONASS or other global navigation system) receiver (not shown) for obtaining information concerning the location and orientation (e.g., portrait or landscape) of device 200.

In some embodiments, the software components stored in memory 202 include operating system 226, communication module (or set of instructions) 228, contact/motion module (or set of instructions) 230, graphics module (or set of instructions) 232, text input module (or set of instructions) 234, Global Positioning System (GPS) module (or set of instructions) 235, Digital Assistant Client Module 229, and applications (or sets of instructions) 236. Further, memory 202 stores data and models, such as user data and models 231. Furthermore, in some embodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/global internal state 257, as shown in FIGS. 2A and 4. Device/global internal state 257 includes one or more of: active application state, indicating which applications, if any, are currently active; display state, indicating what applications, views or other information occupy various regions of touch screen display 212; sensor state, including information obtained from the device's various sensors and input control devices 216; and location information concerning the device's location and/or attitude.

Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components.

Communication module 228 facilitates communication with other devices over one or more external ports 224 and also includes various software components for handling data received by RF circuitry 208 and/or external port 224. External port 224 (e.g., Universal Serial Bus (USB), FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin (e.g., 30-pin) connector that is the same as, or similar to and/or compatible with, the 30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 230 optionally detects contact with touch screen 212 (in conjunction with display controller 256) and other touch-sensitive devices (e.g., a touchpad or physical click wheel). Contact/motion module 230 includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining an intensity of the contact (e.g., the force or pressure of the contact or a substitute for the force or pressure of the contact), determining if there is movement of the contact and tracking the movement across the touch-sensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module 230 receives contact data from the touch-sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, optionally includes determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations are, optionally, applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch”/multiple finger contacts). In some embodiments, contact/motion module 230 and display controller 256 detect contact on a touchpad.

In some embodiments, contact/motion module 230 uses a set of one or more intensity thresholds to determine whether an operation has been performed by a user (e.g., to determine whether a user has “clicked” on an icon). In some embodiments, at least a subset of the intensity thresholds are determined in accordance with software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of particular physical actuators and can be adjusted without changing the physical hardware of device 200). For example, a mouse “click” threshold of a trackpad or touch screen display can be set to any of a large range of predefined threshold values without changing the trackpad or touch screen display hardware. Additionally, in some implementations, a user of the device is provided with software settings for adjusting one or more of the set of intensity thresholds (e.g., by adjusting individual intensity thresholds and/or by adjusting a plurality of intensity thresholds at once with a system-level click “intensity” parameter).

Contact/motion module 230 optionally detects a gesture input by a user. Different gestures on the touch-sensitive surface have different contact patterns (e.g., different motions, timings, and/or intensities of detected contacts). Thus, a gesture is, optionally, detected by detecting a particular contact pattern. For example, detecting a finger tap gesture includes detecting a finger-down event followed by detecting a finger-up (liftoff) event at the same position (or substantially the same position) as the finger-down event (e.g., at the position of an icon). As another example, detecting a finger swipe gesture on the touch-sensitive surface includes detecting a finger-down event followed by detecting one or more finger-dragging events, and subsequently followed by detecting a finger-up (liftoff) event.

Graphics module 232 includes various known software components for rendering and displaying graphics on touch screen 212 or other display, including components for changing the visual impact (e.g., brightness, transparency, saturation, contrast, or other visual property) of graphics that are displayed. As used herein, the term “graphics” includes any object that can be displayed to a user, including, without limitation, text, web pages, icons (such as user-interface objects including soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 232 stores data representing graphics to be used. Each graphic is, optionally, assigned a corresponding code. Graphics module 232 receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller 256.

Haptic feedback module 233 includes various software components for generating instructions used by tactile output generator(s) 267 to produce tactile outputs at one or more locations on device 200 in response to user interactions with device 200.

Text input module 234, which is, in some examples, a component of graphics module 232, provides soft keyboards for entering text in various applications (e.g., contacts 237, email 240, IM 241, browser 247, and any other application that needs text input).

GPS module 235 determines the location of the device and provides this information for use in various applications (e.g., to telephone 238 for use in location-based dialing; to camera 243 as picture/video metadata; and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module 229 includes various client-side digital assistant instructions to provide the client-side functionalities of the digital assistant. For example, digital assistant client module 229 is capable of accepting voice input (e.g., speech input), text input, touch input, and/or gestural input through various user interfaces (e.g., microphone 213, accelerometer(s) 268, touch-sensitive display system 212, optical sensor(s) 264, other input control devices 216, etc.) of portable multifunction device 200. Digital assistant client module 229 is also capable of providing output in audio (e.g., speech output), visual, and/or tactile forms through various output interfaces (e.g., speaker 211, touch-sensitive display system 212, tactile output generator(s) 267, etc.) of portable multifunction device 200. For example, output is provided as voice, sound, alerts, text messages, menus, graphics, videos, animations, vibrations, and/or combinations of two or more of the above. During operation, digital assistant client module 229 communicates with DA server 106 using RF circuitry 208.

User data and models 231 include various data associated with the user (e.g., user-specific vocabulary data, user preference data, user-specified name pronunciations, data from the user's electronic address book, to-do lists, shopping lists, etc.) to provide the client-side functionalities of the digital assistant. Further, user data and models 231 include various models (e.g., speech recognition models, statistical language models, natural language processing models, ontology, task flow models, service models, etc.) for processing user input and determining user intent.

In some examples, digital assistant client module 229 utilizes the various sensors, subsystems, and peripheral devices of portable multifunction device 200 to gather additional information from the surrounding environment of the portable multifunction device 200 to establish a context associated with a user, the current user interaction, and/or the current user input. In some examples, digital assistant client module 229 provides the contextual information or a subset thereof with the user input to DA server 106 to help infer the user's intent. In some examples, the digital assistant also uses the contextual information to determine how to prepare and deliver outputs to the user. Contextual information is referred to as context data.

In some examples, the contextual information that accompanies the user input includes sensor information, e.g., lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some examples, the contextual information can also include the physical state of the device, e.g., device orientation, device location, device temperature, power level, speed, acceleration, motion patterns, cellular signals strength, etc. In some examples, information related to the software state of DA server 106, e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc., and of portable multifunction device 200 is provided to DA server 106 as contextual information associated with a user input.

In some examples, the digital assistant client module 229 selectively provides information (e.g., user data 231) stored on the portable multifunction device 200 in response to requests from DA server 106. In some examples, digital assistant client module 229 also elicits additional input from the user via a natural language dialogue or other user interfaces upon request by DA server 106. Digital assistant client module 229 passes the additional input to DA server 106 to help DA server 106 in intent deduction and/or fulfillment of the user's intent expressed in the user request.

A more detailed description of a digital assistant is described below with reference to FIGS. 7A-C. It should be recognized that digital assistant client module 229 can include any number of the sub-modules of digital assistant module 726 described below.

Applications 236 include the following modules (or sets of instructions), or a subset or superset thereof:

-   -   Contacts module 237 (sometimes called an address book or contact         list);     -   Telephone module 238;     -   Video conference module 239;     -   E-mail client module 240;     -   Instant messaging (IM) module 241;     -   Workout support module 242;     -   Camera module 243 for still and/or video images;     -   Image management module 244;     -   Video player module;     -   Music player module;     -   Browser module 247;     -   Calendar module 248;     -   Widget modules 249, which includes, in some examples, one or         more of: weather widget 249-1, stocks widget 249-2, calculator         widget 249-3, alarm clock widget 249-4, dictionary widget 249-5,         and other widgets obtained by the user, as well as user-created         widgets 249-6;     -   Widget creator module 250 for making user-created widgets 249-6;     -   Search module 251;     -   Video and music player module 252, which merges video player         module and music player module;     -   Notes module 253;     -   Map module 254; and/or     -   Online video module 255.

Examples of other applications 236 that are stored in memory 202 include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication.

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, contacts module 237 are used to manage an address book or contact list (e.g., stored in application internal state 292 of contacts module 237 in memory 202 or memory 470), including: adding name(s) to the address book; deleting name(s) from the address book; associating telephone number(s), e-mail address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or e-mail addresses to initiate and/or facilitate communications by telephone 238, video conference module 239, e-mail 240, or IM 241; and so forth.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, telephone module 238 are used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in contacts module 237, modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As noted above, the wireless communication uses any of a plurality of communications standards, protocols, and technologies.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touch screen 212, display controller 256, optical sensor 264, optical sensor controller 258, contact/motion module 230, graphics module 232, text input module 234, contacts module 237, and telephone module 238, video conference module 239 includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, e-mail client module 240 includes executable instructions to create, send, receive, and manage e-mail in response to user instructions. In conjunction with image management module 244, e-mail client module 240 makes it very easy to create and send e-mails with still or video images taken with camera module 243.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, the instant messaging module 241 includes executable instructions to enter a sequence of characters corresponding to an instant message, to modify previously entered characters, to transmit a respective instant message (for example, using a Short Message Service (SMS) or Multimedia Message Service (MMS) protocol for telephony-based instant messages or using XMPP, SIMPLE, or IMPS for Internet-based instant messages), to receive instant messages, and to view received instant messages. In some embodiments, transmitted and/or received instant messages include graphics, photos, audio files, video files and/or other attachments as are supported in an MMS and/or an Enhanced Messaging Service (EMS). As used herein, “instant messaging” refers to both telephony-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS).

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, GPS module 235, map module 254, and music player module, workout support module 242 includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals); communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store, and transmit workout data.

In conjunction with touch screen 212, display controller 256, optical sensor(s) 264, optical sensor controller 258, contact/motion module 230, graphics module 232, and image management module 244, camera module 243 includes executable instructions to capture still images or video (including a video stream) and store them into memory 202, modify characteristics of a still image or video, or delete a still image or video from memory 202.

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and camera module 243, image management module 244 includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, browser module 247 includes executable instructions to browse the Internet in accordance with user instructions, including searching, linking to, receiving, and displaying web pages or portions thereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, e-mail client module 240, and browser module 247, calendar module 248 includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, to-do lists, etc.) in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and browser module 247, widget modules 249 are mini-applications that can be downloaded and used by a user (e.g., weather widget 249-1, stocks widget 249-2, calculator widget 249-3, alarm clock widget 249-4, and dictionary widget 249-5) or created by the user (e.g., user-created widget 249-6). In some embodiments, a widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., Yahoo! Widgets).

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and browser module 247, the widget creator module 250 are used by a user to create widgets (e.g., turning a user-specified portion of a web page into a widget).

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, search module 251 includes executable instructions to search for text, music, sound, image, video, and/or other files in memory 202 that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, and browser module 247, video and music player module 252 includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen 212 or on an external, connected display via external port 224). In some embodiments, device 200 optionally includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.).

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, notes module 253 includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, GPS module 235, and browser module 247, map module 254 are used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions, data on stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, text input module 234, e-mail client module 240, and browser module 247, online video module 255 includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port 224), send an e-mail with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module 241, rather than e-mail client module 240, is used to send a link to a particular online video. Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Jun. 20, 2007, and U.S. patent application Ser. No. 11/968,067, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Dec. 31, 2007, the contents of which are hereby incorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules can be combined or otherwise rearranged in various embodiments. For example, video player module can be combined with music player module into a single module (e.g., video and music player module 252, FIG. 2A). In some embodiments, memory 202 stores a subset of the modules and data structures identified above. Furthermore, memory 202 stores additional modules and data structures not described above.

In some embodiments, device 200 is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device 200, the number of physical input control devices (such as push buttons, dials, and the like) on device 200 is reduced.

The predefined set of functions that are performed exclusively through a touch screen and/or a touchpad optionally include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device 200 to a main, home, or root menu from any user interface that is displayed on device 200. In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad.

FIG. 2B is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. In some embodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., in operating system 226) and a respective application 236-1 (e.g., any of the aforementioned applications 237-251, 255, 480-490).

Event sorter 270 receives event information and determines the application 236-1 and application view 291 of application 236-1 to which to deliver the event information. Event sorter 270 includes event monitor 271 and event dispatcher module 274. In some embodiments, application 236-1 includes application internal state 292, which indicates the current application view(s) displayed on touch-sensitive display 212 when the application is active or executing. In some embodiments, device/global internal state 257 is used by event sorter 270 to determine which application(s) is (are) currently active, and application internal state 292 is used by event sorter 270 to determine application views 291 to which to deliver event information.

In some embodiments, application internal state 292 includes additional information, such as one or more of: resume information to be used when application 236-1 resumes execution, user interface state information that indicates information being displayed or that is ready for display by application 236-1, a state queue for enabling the user to go back to a prior state or view of application 236-1, and a redo/undo queue of previous actions taken by the user.

Event monitor 271 receives event information from peripherals interface 218. Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display 212, as part of a multi-touch gesture). Peripherals interface 218 transmits information it receives from I/O subsystem 206 or a sensor, such as proximity sensor 266, accelerometer(s) 268, and/or microphone 213 (through audio circuitry 210). Information that peripherals interface 218 receives from I/O subsystem 206 includes information from touch-sensitive display 212 or a touch-sensitive surface.

In some embodiments, event monitor 271 sends requests to the peripherals interface 218 at predetermined intervals. In response, peripherals interface 218 transmits event information. In other embodiments, peripherals interface 218 transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 270 also includes a hit view determination module 272 and/or an active event recognizer determination module 273.

Hit view determination module 272 provides software procedures for determining where a sub-event has taken place within one or more views when touch-sensitive display 212 displays more than one view. Views are made up of controls and other elements that a user can see on the display.

Another aspect of the user interface associated with an application is a set of views, sometimes herein called application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of a respective application) in which a touch is detected correspond to programmatic levels within a programmatic or view hierarchy of the application. For example, the lowest level view in which a touch is detected is called the hit view, and the set of events that are recognized as proper inputs is determined based, at least in part, on the hit view of the initial touch that begins a touch-based gesture.

Hit view determination module 272 receives information related to sub events of a touch-based gesture. When an application has multiple views organized in a hierarchy, hit view determination module 272 identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination module 272, the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view.

Active event recognizer determination module 273 determines which view or views within a view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module 273 determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module 273 determines that all views that include the physical location of a sub-event are actively involved views, and therefore determines that all actively involved views should receive a particular sequence of sub-events. In other embodiments, even if touch sub-events were entirely confined to the area associated with one particular view, views higher in the hierarchy would still remain as actively involved views.

Event dispatcher module 274 dispatches the event information to an event recognizer (e.g., event recognizer 280). In embodiments including active event recognizer determination module 273, event dispatcher module 274 delivers the event information to an event recognizer determined by active event recognizer determination module 273. In some embodiments, event dispatcher module 274 stores in an event queue the event information, which is retrieved by a respective event receiver 282.

In some embodiments, operating system 226 includes event sorter 270. Alternatively, application 236-1 includes event sorter 270. In yet other embodiments, event sorter 270 is a stand-alone module, or a part of another module stored in memory 202, such as contact/motion module 230.

In some embodiments, application 236-1 includes a plurality of event handlers 290 and one or more application views 291, each of which includes instructions for handling touch events that occur within a respective view of the application's user interface. Each application view 291 of the application 236-1 includes one or more event recognizers 280. Typically, a respective application view 291 includes a plurality of event recognizers 280. In other embodiments, one or more of event recognizers 280 are part of a separate module, such as a user interface kit (not shown) or a higher level object from which application 236-1 inherits methods and other properties. In some embodiments, a respective event handler 290 includes one or more of: data updater 276, object updater 277, GUI updater 278, and/or event data 279 received from event sorter 270. Event handler 290 utilizes or calls data updater 276, object updater 277, or GUI updater 278 to update the application internal state 292. Alternatively, one or more of the application views 291 include one or more respective event handlers 290. Also, in some embodiments, one or more of data updater 276, object updater 277, and GUI updater 278 are included in a respective application view 291.

A respective event recognizer 280 receives event information (e.g., event data 279) from event sorter 270 and identifies an event from the event information. Event recognizer 280 includes event receiver 282 and event comparator 284. In some embodiments, event recognizer 280 also includes at least a subset of: metadata 283, and event delivery instructions 288 (which include sub-event delivery instructions).

Event receiver 282 receives event information from event sorter 270. The event information includes information about a sub-event, for example, a touch or a touch movement. Depending on the sub-event, the event information also includes additional information, such as location of the sub-event. When the sub-event concerns motion of a touch, the event information also includes speed and direction of the sub-event. In some embodiments, events include rotation of the device from one orientation to another (e.g., from a portrait orientation to a landscape orientation, or vice versa), and the event information includes corresponding information about the current orientation (also called device attitude) of the device.

Event comparator 284 compares the event information to predefined event or sub-event definitions and, based on the comparison, determines an event or sub event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator 284 includes event definitions 286. Event definitions 286 contain definitions of events (e.g., predefined sequences of sub-events), for example, event 1 (287-1), event 2 (287-2), and others. In some embodiments, sub-events in an event (287) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event 1 (287-1) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event 2 (287-2) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display 212, and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers 290.

In some embodiments, event definition 287 includes a definition of an event for a respective user-interface object. In some embodiments, event comparator 284 performs a hit test to determine which user-interface object is associated with a sub-event. For example, in an application view in which three user-interface objects are displayed on touch-sensitive display 212, when a touch is detected on touch-sensitive display 212, event comparator 284 performs a hit test to determine which of the three user-interface objects is associated with the touch (sub-event). If each displayed object is associated with a respective event handler 290, the event comparator uses the result of the hit test to determine which event handler 290 should be activated. For example, event comparator 284 selects an event handler associated with the sub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer's event type.

When a respective event recognizer 280 determines that the series of sub-events do not match any of the events in event definitions 286, the respective event recognizer 280 enters an event impossible, event failed, or event ended state, after which it disregards subsequent sub-events of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 280 includes metadata 283 with configurable properties, flags, and/or lists that indicate how the event delivery system should perform sub-event delivery to actively involved event recognizers. In some embodiments, metadata 283 includes configurable properties, flags, and/or lists that indicate how event recognizers interact, or are enabled to interact, with one another. In some embodiments, metadata 283 includes configurable properties, flags, and/or lists that indicate whether sub-events are delivered to varying levels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 280 activates event handler 290 associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer 280 delivers event information associated with the event to event handler 290. Activating an event handler 290 is distinct from sending (and deferred sending) sub-events to a respective hit view. In some embodiments, event recognizer 280 throws a flag associated with the recognized event, and event handler 290 associated with the flag catches the flag and performs a predefined process.

In some embodiments, event delivery instructions 288 include sub-event delivery instructions that deliver event information about a sub-event without activating an event handler. Instead, the sub-event delivery instructions deliver event information to event handlers associated with the series of sub-events or to actively involved views. Event handlers associated with the series of sub-events or with actively involved views receive the event information and perform a predetermined process.

In some embodiments, data updater 276 creates and updates data used in application 236-1. For example, data updater 276 updates the telephone number used in contacts module 237, or stores a video file used in video player module. In some embodiments, object updater 277 creates and updates objects used in application 236-1. For example, object updater 277 creates a new user-interface object or updates the position of a user-interface object. GUI updater 278 updates the GUI. For example, GUI updater 278 prepares display information and sends it to graphics module 232 for display on a touch-sensitive display.

In some embodiments, event handler(s) 290 includes or has access to data updater 276, object updater 277, and GUI updater 278. In some embodiments, data updater 276, object updater 277, and GUI updater 278 are included in a single module of a respective application 236-1 or application view 291. In other embodiments, they are included in two or more software modules.

It shall be understood that the foregoing discussion regarding event handling of user touches on touch-sensitive displays also applies to other forms of user inputs to operate multifunction devices 200 with input devices, not all of which are initiated on touch screens. For example, mouse movement and mouse button presses, optionally coordinated with single or multiple keyboard presses or holds; contact movements such as taps, drags, scrolls, etc. on touchpads; pen stylus inputs; movement of the device; oral instructions; detected eye movements; biometric inputs; and/or any combination thereof are optionally utilized as inputs corresponding to sub-events which define an event to be recognized.

FIG. 3 illustrates a portable multifunction device 200 having a touch screen 212 in accordance with some embodiments. The touch screen optionally displays one or more graphics within user interface (UI) 300. In this embodiment, as well as others described below, a user is enabled to select one or more of the graphics by making a gesture on the graphics, for example, with one or more fingers 302 (not drawn to scale in the figure) or one or more styluses 303 (not drawn to scale in the figure). In some embodiments, selection of one or more graphics occurs when the user breaks contact with the one or more graphics. In some embodiments, the gesture optionally includes one or more taps, one or more swipes (from left to right, right to left, upward and/or downward), and/or a rolling of a finger (from right to left, left to right, upward and/or downward) that has made contact with device 200. In some implementations or circumstances, inadvertent contact with a graphic does not select the graphic. For example, a swipe gesture that sweeps over an application icon optionally does not select the corresponding application when the gesture corresponding to selection is a tap.

Device 200 also includes one or more physical buttons, such as “home” or menu button 304. As described previously, menu button 304 is used to navigate to any application 236 in a set of applications that is executed on device 200. Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen 212.

In one embodiment, device 200 includes touch screen 212, menu button 304, push button 306 for powering the device on/off and locking the device, volume adjustment button(s) 308, subscriber identity module (SIM) card slot 310, headset jack 312, and docking/charging external port 224. Push button 306 is, optionally, used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device 200 also accepts verbal input for activation or deactivation of some functions through microphone 213. Device 200 also, optionally, includes one or more contact intensity sensors 265 for detecting intensity of contacts on touch screen 212 and/or one or more tactile output generators 267 for generating tactile outputs for a user of device 200.

FIG. 4 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. Device 400 need not be portable. In some embodiments, device 400 is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a child's learning toy), a gaming system, or a control device (e.g., a home or industrial controller). Device 400 typically includes one or more processing units (CPUs) 410, one or more network or other communications interfaces 460, memory 470, and one or more communication buses 420 for interconnecting these components. Communication buses 420 optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Device 400 includes input/output (I/O) interface 430 comprising display 440, which is typically a touch screen display. I/O interface 430 also optionally includes a keyboard and/or mouse (or other pointing device) 450 and touchpad 455, tactile output generator 457 for generating tactile outputs on device 400 (e.g., similar to tactile output generator(s) 267 described above with reference to FIG. 2A), sensors 459 (e.g., optical, acceleration, proximity, touch-sensitive, and/or contact intensity sensors similar to contact intensity sensor(s) 265 described above with reference to FIG. 2A). Memory 470 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 470 optionally includes one or more storage devices remotely located from CPU(s) 410. In some embodiments, memory 470 stores programs, modules, and data structures analogous to the programs, modules, and data structures stored in memory 202 of portable multifunction device 200 (FIG. 2A), or a subset thereof. Furthermore, memory 470 optionally stores additional programs, modules, and data structures not present in memory 202 of portable multifunction device 200. For example, memory 470 of device 400 optionally stores drawing module 480, presentation module 482, word processing module 484, website creation module 486, disk authoring module 488, and/or spreadsheet module 490, while memory 202 of portable multifunction device 200 (FIG. 2A) optionally does not store these modules.

Each of the above-identified elements in FIG. 4 is, in some examples, stored in one or more of the previously mentioned memory devices. Each of the above-identified modules corresponds to a set of instructions for performing a function described above. The above-identified modules or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules are combined or otherwise rearranged in various embodiments. In some embodiments, memory 470 stores a subset of the modules and data structures identified above. Furthermore, memory 470 stores additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces that can be implemented on, for example, portable multifunction device 200.

FIG. 5A illustrates an exemplary user interface for a menu of applications on portable multifunction device 200 in accordance with some embodiments. Similar user interfaces are implemented on device 400. In some embodiments, user interface 500 includes the following elements, or a subset or superset thereof:

Signal strength indicator(s) 502 for wireless communication(s), such as cellular and Wi-Fi signals;

-   -   Time 504;     -   Bluetooth indicator 505;     -   Battery status indicator 506;     -   Tray 508 with icons for frequently used applications, such as:         -   Icon 516 for telephone module 238, labeled “Phone,” which             optionally includes an indicator 514 of the number of missed             calls or voicemail messages;         -   Icon 518 for e-mail client module 240, labeled “Mail,” which             optionally includes an indicator 510 of the number of unread             e-mails;         -   Icon 520 for browser module 247, labeled “Browser;” and         -   Icon 522 for video and music player module 252, also             referred to as iPod (trademark of Apple Inc.) module 252,             labeled “iPod;” and     -   Icons for other applications, such as:         -   Icon 524 for IM module 241, labeled “Messages;”         -   Icon 526 for calendar module 248, labeled “Calendar;”         -   Icon 528 for image management module 244, labeled “Photos;”         -   Icon 530 for camera module 243, labeled “Camera;”         -   Icon 532 for online video module 255, labeled “Online             Video;”         -   Icon 534 for stocks widget 249-2, labeled “Stocks;”         -   Icon 536 for map module 254, labeled “Maps;”         -   Icon 538 for weather widget 249-1, labeled “Weather;”         -   Icon 540 for alarm clock widget 249-4, labeled “Clock;”         -   Icon 542 for workout support module 242, labeled “Workout             Support;”         -   Icon 544 for notes module 253, labeled “Notes;” and         -   Icon 546 for a settings application or module, labeled             “Settings,” which provides access to settings for device 200             and its various applications 236.

It should be noted that the icon labels illustrated in FIG. 5A are merely exemplary. For example, icon 522 for video and music player module 252 is optionally labeled “Music” or “Music Player.” Other labels are, optionally, used for various application icons. In some embodiments, a label for a respective application icon includes a name of an application corresponding to the respective application icon. In some embodiments, a label for a particular application icon is distinct from a name of an application corresponding to the particular application icon.

FIG. 5B illustrates an exemplary user interface on a device (e.g., device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tablet or touchpad 455, FIG. 4) that is separate from the display 550 (e.g., touch screen display 212). Device 400 also, optionally, includes one or more contact intensity sensors (e.g., one or more of sensors 457) for detecting intensity of contacts on touch-sensitive surface 551 and/or one or more tactile output generators 459 for generating tactile outputs for a user of device 400.

Although some of the examples which follow will be given with reference to inputs on touch screen display 212 (where the touch-sensitive surface and the display are combined), in some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown in FIG. 5B. In some embodiments, the touch-sensitive surface (e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) that corresponds to a primary axis (e.g., 553 in FIG. 5B) on the display (e.g., 550). In accordance with these embodiments, the device detects contacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface 551 at locations that correspond to respective locations on the display (e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570). In this way, user inputs (e.g., contacts 560 and 562, and movements thereof) detected by the device on the touch-sensitive surface (e.g., 551 in FIG. 5B) are used by the device to manipulate the user interface on the display (e.g., 550 in FIG. 5B) of the multifunction device when the touch-sensitive surface is separate from the display. It should be understood that similar methods are, optionally, used for other user interfaces described herein.

Additionally, while the following examples are given primarily with reference to finger inputs (e.g., finger contacts, finger tap gestures, finger swipe gestures), it should be understood that, in some embodiments, one or more of the finger inputs are replaced with input from another input device (e.g., a mouse-based input or stylus input). For example, a swipe gesture is, optionally, replaced with a mouse click (e.g., instead of a contact) followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is, optionally, replaced with a mouse click while the cursor is located over the location of the tap gesture (e.g., instead of detection of the contact followed by ceasing to detect the contact). Similarly, when multiple user inputs are simultaneously detected, it should be understood that multiple computer mice are, optionally, used simultaneously, or a mouse and finger contacts are, optionally, used simultaneously.

FIG. 6A illustrates exemplary personal electronic device 600. Device 600 includes body 602. In some embodiments, device 600 includes some or all of the features described with respect to devices 200 and 400 (e.g., FIGS. 2A-4). In some embodiments, device 600 has touch-sensitive display screen 604, hereafter touch screen 604. Alternatively, or in addition to touch screen 604, device 600 has a display and a touch-sensitive surface. As with devices 200 and 400, in some embodiments, touch screen 604 (or the touch-sensitive surface) has one or more intensity sensors for detecting intensity of contacts (e.g., touches) being applied. The one or more intensity sensors of touch screen 604 (or the touch-sensitive surface) provide output data that represents the intensity of touches. The user interface of device 600 responds to touches based on their intensity, meaning that touches of different intensities can invoke different user interface operations on device 600.

Techniques for detecting and processing touch intensity are found, for example, in related applications: International Patent Application Serial No. PCT/US2013/040061, titled “Device, Method, and Graphical User Interface for Displaying User Interface Objects Corresponding to an Application,” filed May 8, 2013, and International Patent Application Serial No. PCT/US2013/069483, titled “Device, Method, and Graphical User Interface for Transitioning Between Touch Input to Display Output Relationships,” filed Nov. 11, 2013, each of which is hereby incorporated by reference in their entirety.

In some embodiments, device 600 has one or more input mechanisms 606 and 608. Input mechanisms 606 and 608, if included, are physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, device 600 has one or more attachment mechanisms. Such attachment mechanisms, if included, can permit attachment of device 600 with, for example, hats, eyewear, earrings, necklaces, shirts, jackets, bracelets, watch straps, chains, trousers, belts, shoes, purses, backpacks, and so forth. These attachment mechanisms permit device 600 to be worn by a user.

FIG. 6B depicts exemplary personal electronic device 600. In some embodiments, device 600 includes some or all of the components described with respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612 that operatively couples I/O section 614 with one or more computer processors 616 and memory 618. I/O section 614 is connected to display 604, which can have touch-sensitive component 622 and, optionally, touch-intensity sensitive component 624. In addition, I/O section 614 is connected with communication unit 630 for receiving application and operating system data, using Wi-Fi, Bluetooth, near field communication (NFC), cellular, and/or other wireless communication techniques. Device 600 includes input mechanisms 606 and/or 608. Input mechanism 606 is a rotatable input device or a depressible and rotatable input device, for example. Input mechanism 608 is a button, in some examples.

Input mechanism 608 is a microphone, in some examples. Personal electronic device 600 includes, for example, various sensors, such as GPS sensor 632, accelerometer 634, directional sensor 640 (e.g., compass), gyroscope 636, motion sensor 638, and/or a combination thereof, all of which are operatively connected to I/O section 614.

Memory 618 of personal electronic device 600 is a non-transitory computer-readable storage medium, for storing computer-executable instructions, which, when executed by one or more computer processors 616, for example, cause the computer processors to perform the techniques and processes described below. The computer-executable instructions, for example, are also stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. Personal electronic device 600 is not limited to the components and configuration of FIG. 6B, but can include other or additional components in multiple configurations.

As used here, the term “affordance” refers to a user-interactive graphical user interface object that is, for example, displayed on the display screen of devices 200, 400, 600, 1100 and/or 1300 (FIGS. 2A, 4, 6A-B, 11, and 13A-B). For example, an image (e.g., icon), a button, and text (e.g., hyperlink) each constitutes an affordance.

As used herein, the term “focus selector” refers to an input element that indicates a current part of a user interface with which a user is interacting. In some implementations that include a cursor or other location marker, the cursor acts as a “focus selector” so that when an input (e.g., a press input) is detected on a touch-sensitive surface (e.g., touchpad 455 in FIG. 4 or touch-sensitive surface 551 in FIG. 5B) while the cursor is over a particular user interface element (e.g., a button, window, slider or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations that include a touch screen display (e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212 in FIG. 5A) that enables direct interaction with user interface elements on the touch screen display, a detected contact on the touch screen acts as a “focus selector” so that when an input (e.g., a press input by the contact) is detected on the touch screen display at a location of a particular user interface element (e.g., a button, window, slider, or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations, focus is moved from one region of a user interface to another region of the user interface without corresponding movement of a cursor or movement of a contact on a touch screen display (e.g., by using a tab key or arrow keys to move focus from one button to another button); in these implementations, the focus selector moves in accordance with movement of focus between different regions of the user interface. Without regard to the specific form taken by the focus selector, the focus selector is generally the user interface element (or contact on a touch screen display) that is controlled by the user so as to communicate the user's intended interaction with the user interface (e.g., by indicating, to the device, the element of the user interface with which the user is intending to interact). For example, the location of a focus selector (e.g., a cursor, a contact, or a selection box) over a respective button while a press input is detected on the touch-sensitive surface (e.g., a touchpad or touch screen) will indicate that the user is intending to activate the respective button (as opposed to other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristic intensity” of a contact refers to a characteristic of the contact based on one or more intensities of the contact. In some embodiments, the characteristic intensity is based on multiple intensity samples. The characteristic intensity is, optionally, based on a predefined number of intensity samples, or a set of intensity samples collected during a predetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10 seconds) relative to a predefined event (e.g., after detecting the contact, prior to detecting liftoff of the contact, before or after detecting a start of movement of the contact, prior to detecting an end of the contact, before or after detecting an increase in intensity of the contact, and/or before or after detecting a decrease in intensity of the contact). A characteristic intensity of a contact is, optionally based on one or more of: a maximum value of the intensities of the contact, a mean value of the intensities of the contact, an average value of the intensities of the contact, a top 10 percentile value of the intensities of the contact, a value at the half maximum of the intensities of the contact, a value at the 90 percent maximum of the intensities of the contact, or the like. In some embodiments, the duration of the contact is used in determining the characteristic intensity (e.g., when the characteristic intensity is an average of the intensity of the contact over time). In some embodiments, the characteristic intensity is compared to a set of one or more intensity thresholds to determine whether an operation has been performed by a user. For example, the set of one or more intensity thresholds includes a first intensity threshold and a second intensity threshold. In this example, a contact with a characteristic intensity that does not exceed the first threshold results in a first operation, a contact with a characteristic intensity that exceeds the first intensity threshold and does not exceed the second intensity threshold results in a second operation, and a contact with a characteristic intensity that exceeds the second threshold results in a third operation. In some embodiments, a comparison between the characteristic intensity and one or more thresholds is used to determine whether or not to perform one or more operations (e.g., whether to perform a respective operation or forgo performing the respective operation) rather than being used to determine whether to perform a first operation or a second operation.

In some embodiments, a portion of a gesture is identified for purposes of determining a characteristic intensity. For example, a touch-sensitive surface receives a continuous swipe contact transitioning from a start location and reaching an end location, at which point the intensity of the contact increases. In this example, the characteristic intensity of the contact at the end location is based on only a portion of the continuous swipe contact, and not the entire swipe contact (e.g., only the portion of the swipe contact at the end location). In some embodiments, a smoothing algorithm is applied to the intensities of the swipe contact prior to determining the characteristic intensity of the contact. For example, the smoothing algorithm optionally includes one or more of: an unweighted sliding-average smoothing algorithm, a triangular smoothing algorithm, a median filter smoothing algorithm, and/or an exponential smoothing algorithm. In some circumstances, these smoothing algorithms eliminate narrow spikes or dips in the intensities of the swipe contact for purposes of determining a characteristic intensity.

The intensity of a contact on the touch-sensitive surface is characterized relative to one or more intensity thresholds, such as a contact-detection intensity threshold, a light press intensity threshold, a deep press intensity threshold, and/or one or more other intensity thresholds. In some embodiments, the light press intensity threshold corresponds to an intensity at which the device will perform operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device will perform operations that are different from operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, when a contact is detected with a characteristic intensity below the light press intensity threshold (e.g., and above a nominal contact-detection intensity threshold below which the contact is no longer detected), the device will move a focus selector in accordance with movement of the contact on the touch-sensitive surface without performing an operation associated with the light press intensity threshold or the deep press intensity threshold. Generally, unless otherwise stated, these intensity thresholds are consistent between different sets of user interface figures.

An increase of characteristic intensity of the contact from an intensity below the light press intensity threshold to an intensity between the light press intensity threshold and the deep press intensity threshold is sometimes referred to as a “light press” input. An increase of characteristic intensity of the contact from an intensity below the deep press intensity threshold to an intensity above the deep press intensity threshold is sometimes referred to as a “deep press” input. An increase of characteristic intensity of the contact from an intensity below the contact-detection intensity threshold to an intensity between the contact-detection intensity threshold and the light press intensity threshold is sometimes referred to as detecting the contact on the touch-surface. A decrease of characteristic intensity of the contact from an intensity above the contact-detection intensity threshold to an intensity below the contact-detection intensity threshold is sometimes referred to as detecting liftoff of the contact from the touch-surface. In some embodiments, the contact-detection intensity threshold is zero. In some embodiments, the contact-detection intensity threshold is greater than zero.

In some embodiments described herein, one or more operations are performed in response to detecting a gesture that includes a respective press input or in response to detecting the respective press input performed with a respective contact (or a plurality of contacts), where the respective press input is detected based at least in part on detecting an increase in intensity of the contact (or plurality of contacts) above a press-input intensity threshold. In some embodiments, the respective operation is performed in response to detecting the increase in intensity of the respective contact above the press-input intensity threshold (e.g., a “down stroke” of the respective press input). In some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the press-input threshold (e.g., an “up stroke” of the respective press input).

In some embodiments, the device employs intensity hysteresis to avoid accidental inputs sometimes termed “jitter,” where the device defines or selects a hysteresis intensity threshold with a predefined relationship to the press-input intensity threshold (e.g., the hysteresis intensity threshold is X intensity units lower than the press-input intensity threshold or the hysteresis intensity threshold is 75%, 90%, or some reasonable proportion of the press-input intensity threshold). Thus, in some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the hysteresis intensity threshold that corresponds to the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the hysteresis intensity threshold (e.g., an “up stroke” of the respective press input). Similarly, in some embodiments, the press input is detected only when the device detects an increase in intensity of the contact from an intensity at or below the hysteresis intensity threshold to an intensity at or above the press-input intensity threshold and, optionally, a subsequent decrease in intensity of the contact to an intensity at or below the hysteresis intensity, and the respective operation is performed in response to detecting the press input (e.g., the increase in intensity of the contact or the decrease in intensity of the contact, depending on the circumstances).

For ease of explanation, the descriptions of operations performed in response to a press input associated with a press-input intensity threshold or in response to a gesture including the press input are, optionally, triggered in response to detecting either: an increase in intensity of a contact above the press-input intensity threshold, an increase in intensity of a contact from an intensity below the hysteresis intensity threshold to an intensity above the press-input intensity threshold, a decrease in intensity of the contact below the press-input intensity threshold, and/or a decrease in intensity of the contact below the hysteresis intensity threshold corresponding to the press-input intensity threshold. Additionally, in examples where an operation is described as being performed in response to detecting a decrease in intensity of a contact below the press-input intensity threshold, the operation is, optionally, performed in response to detecting a decrease in intensity of the contact below a hysteresis intensity threshold corresponding to, and lower than, the press-input intensity threshold.

3. Digital Assistant System

FIG. 7A illustrates a block diagram of digital assistant system 700 in accordance with various examples. In some examples, digital assistant system 700 is implemented on a standalone computer system. In some examples, digital assistant system 700 is distributed across multiple computers. In some examples, some of the modules and functions of the digital assistant are divided into a server portion and a client portion, where the client portion resides on one or more user devices (e.g., devices 104, 122, 200, 400, 600, 1100, or 1300) and communicates with the server portion (e.g., server system 108) through one or more networks, e.g., as shown in FIG. 1. In some examples, digital assistant system 700 is an implementation of server system 108 (and/or DA server 106) shown in FIG. 1. It should be noted that digital assistant system 700 is only one example of a digital assistant system, and that digital assistant system 700 can have more or fewer components than shown, can combine two or more components, or can have a different configuration or arrangement of the components. The various components shown in FIG. 7A are implemented in hardware, software instructions for execution by one or more processors, firmware, including one or more signal processing and/or application specific integrated circuits, or a combination thereof.

Digital assistant system 700 includes memory 702, one or more processors 704, input/output (I/O) interface 706, and network communications interface 708. These components can communicate with one another over one or more communication buses or signal lines 710.

In some examples, memory 702 includes a non-transitory computer-readable medium, such as high-speed random access memory and/or a non-volatile computer-readable storage medium (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

In some examples, I/O interface 706 couples input/output devices 716 of digital assistant system 700, such as displays, keyboards, touch screens, and microphones, to user interface module 722. I/O interface 706, in conjunction with user interface module 722, receives user inputs (e.g., voice input, keyboard inputs, touch inputs, etc.) and processes them accordingly. In some examples, e.g., when the digital assistant is implemented on a standalone user device, digital assistant system 700 includes any of the components and I/O communication interfaces described with respect to devices 200, 400, 600, 1100, or 1300 in FIGS. 2A, 4, 6A-B, 11, and 13A-B respectively. In some examples, digital assistant system 700 represents the server portion of a digital assistant implementation, and can interact with the user through a client-side portion residing on a user device (e.g., devices 104, 200, 400, 600, 1100, or 1300).

In some examples, the network communications interface 708 includes wired communication port(s) 712 and/or wireless transmission and reception circuitry 714. The wired communication port(s) receives and send communication signals via one or more wired interfaces, e.g., Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitry 714 receives and sends RF signals and/or optical signals from/to communications networks and other communications devices. The wireless communications use any of a plurality of communications standards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol. Network communications interface 708 enables communication between digital assistant system 700 with networks, such as the Internet, an intranet, and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN), and/or a metropolitan area network (MAN), and other devices.

In some examples, memory 702, or the computer-readable storage media of memory 702, stores programs, modules, instructions, and data structures including all or a subset of: operating system 718, communications module 720, user interface module 722, one or more applications 724, and digital assistant module 726. In particular, memory 702, or the computer-readable storage media of memory 702, stores instructions for performing the processes described below. One or more processors 704 execute these programs, modules, and instructions, and reads/writes from/to the data structures.

Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communications between various hardware, firmware, and software components.

Communications module 720 facilitates communications between digital assistant system 700 with other devices over network communications interface 708. For example, communications module 720 communicates with RF circuitry 208 of electronic devices such as devices 200, 400, and 600 shown in FIGS. 2A, 4, 6A-B, respectively. Communications module 720 also includes various components for handling data received by wireless circuitry 714 and/or wired communications port 712.

User interface module 722 receives commands and/or inputs from a user via I/O interface 706 (e.g., from a keyboard, touch screen, pointing device, controller, and/or microphone), and generate user interface objects on a display. User interface module 722 also prepares and delivers outputs (e.g., speech, sound, animation, text, icons, vibrations, haptic feedback, light, etc.) to the user via the I/O interface 706 (e.g., through displays, audio channels, speakers, touch-pads, etc.).

Applications 724 include programs and/or modules that are configured to be executed by one or more processors 704. For example, if the digital assistant system is implemented on a standalone user device, applications 724 include user applications, such as games, a calendar application, a navigation application, or an email application. If digital assistant system 700 is implemented on a server, applications 724 include resource management applications, diagnostic applications, or scheduling applications, for example.

Memory 702 also stores digital assistant module 726 (or the server portion of a digital assistant). In some examples, digital assistant module 726 includes the following sub-modules, or a subset or superset thereof: input/output processing module 728, speech-to-text (STT) processing module 730, natural language processing module 732, dialogue flow processing module 734, task flow processing module 736, service processing module 738, and speech synthesis processing module 740. Each of these modules has access to one or more of the following systems or data and models of the digital assistant module 726, or a subset or superset thereof: ontology 760, vocabulary index 744, user data 748, task flow models 754, service models 756, and ASR systems 758.

In some examples, using the processing modules, data, and models implemented in digital assistant module 726, the digital assistant can perform at least some of the following: converting speech input into text; identifying a user's intent expressed in a natural language input received from the user; actively eliciting and obtaining information needed to fully infer the user's intent (e.g., by disambiguating words, games, intentions, etc.); determining the task flow for fulfilling the inferred intent; and executing the task flow to fulfill the inferred intent.

In some examples, as shown in FIG. 7B, I/O processing module 728 interacts with the user through I/O devices 716 in FIG. 7A or with a user device (e.g., devices 104, 200, 400, or 600) through network communications interface 708 in FIG. 7A to obtain user input (e.g., a speech input) and to provide responses (e.g., as speech outputs) to the user input. I/O processing module 728 optionally obtains contextual information associated with the user input from the user device, along with or shortly after the receipt of the user input. The contextual information includes user-specific data, vocabulary, and/or preferences relevant to the user input. In some examples, the contextual information also includes software and hardware states of the user device at the time the user request is received, and/or information related to the surrounding environment of the user at the time that the user request was received. In some examples, I/O processing module 728 also sends follow-up questions to, and receive answers from, the user regarding the user request. When a user request is received by I/O processing module 728 and the user request includes speech input, I/O processing module 728 forwards the speech input to STT processing module 730 (or speech recognizer) for speech-to-text conversions.

STT processing module 730 includes one or more ASR systems 758. The one or more ASR systems 758 can process the speech input that is received through I/O processing module 728 to produce a recognition result. Each ASR system 758 includes a front-end speech pre-processor. The front-end speech pre-processor extracts representative features from the speech input. For example, the front-end speech pre-processor performs a Fourier transform on the speech input to extract spectral features that characterize the speech input as a sequence of representative multi-dimensional vectors. Further, each ASR system 758 includes one or more speech recognition models (e.g., acoustic models and/or language models) and implements one or more speech recognition engines. Examples of speech recognition models include Hidden Markov Models, Gaussian-Mixture Models, Deep Neural Network Models, n-gram language models, and other statistical models. Examples of speech recognition engines include the dynamic time warping based engines and weighted finite-state transducers (WFST) based engines. The one or more speech recognition models and the one or more speech recognition engines are used to process the extracted representative features of the front-end speech pre-processor to produce intermediate recognitions results (e.g., phonemes, phonemic strings, and sub-words), and ultimately, text recognition results (e.g., words, word strings, or sequence of tokens). In some examples, the speech input is processed at least partially by a third-party service or on the user's device (e.g., device 104, 200, 400, or 600) to produce the recognition result. Once STT processing module 730 produces recognition results containing a text string (e.g., words, or sequence of words, or sequence of tokens), the recognition result is passed to natural language processing module 732 for intent deduction. In some examples, STT processing module 730 produces multiple candidate text representations of the speech input. Each candidate text representation is a sequence of words or tokens corresponding to the speech input. In some examples, each candidate text representation is associated with a speech recognition confidence score. Based on the speech recognition confidence scores, STT processing module 730 ranks the candidate text representations and provides the n-best (e.g., n highest ranked) candidate text representation(s) to natural language processing module 732 for intent deduction, where n is a predetermined integer greater than zero. For example, in one example, only the highest ranked (n=1) candidate text representation is passed to natural language processing module 732 for intent deduction. In another example, the five highest ranked (n=5) candidate text representations are passed to natural language processing module 732 for intent deduction.

More details on the speech-to-text processing are described in U.S. Utility application Ser. No. 13/236,942 for “Consolidating Speech Recognition Results,” filed on Sep. 20, 2011, the entire disclosure of which is incorporated herein by reference.

In some examples, STT processing module 730 includes and/or accesses a vocabulary of recognizable words via phonetic alphabet conversion module 731. Each vocabulary word is associated with one or more candidate pronunciations of the word represented in a speech recognition phonetic alphabet. In particular, the vocabulary of recognizable words includes a word that is associated with a plurality of candidate pronunciations. For example, the vocabulary includes the word “tomato” that is associated with the candidate pronunciations of /

/ and /

/. Further, vocabulary words are associated with custom candidate pronunciations that are based on previous speech inputs from the user. Such custom candidate pronunciations are stored in STT processing module 730 and are associated with a particular user via the user's profile on the device. In some examples, the candidate pronunciations for words are determined based on the spelling of the word and one or more linguistic and/or phonetic rules. In some examples, the candidate pronunciations are manually generated, e.g., based on known canonical pronunciations.

In some examples, the candidate pronunciations are ranked based on the commonness of the candidate pronunciation. For example, the candidate pronunciation /

/ is ranked higher than /

/, because the former is a more commonly used pronunciation (e.g., among all users, for users in a particular geographical region, or for any other appropriate subset of users). In some examples, candidate pronunciations are ranked based on whether the candidate pronunciation is a custom candidate pronunciation associated with the user. For example, custom candidate pronunciations are ranked higher than canonical candidate pronunciations. This can be useful for recognizing proper nouns having a unique pronunciation that deviates from canonical pronunciation. In some examples, candidate pronunciations are associated with one or more speech characteristics, such as geographic origin, nationality, or ethnicity. For example, the candidate pronunciation /

/ is associated with the United States, whereas the candidate pronunciation /

/ is associated with Great Britain. Further, the rank of the candidate pronunciation is based on one or more characteristics (e.g., geographic origin, nationality, ethnicity, etc.) of the user stored in the user's profile on the device. For example, it can be determined from the user's profile that the user is associated with the United States. Based on the user being associated with the United States, the candidate pronunciation /

/ (associated with the United States) is ranked higher than the candidate pronunciation /

/ (associated with Great Britain). In some examples, one of the ranked candidate pronunciations is selected as a predicted pronunciation (e.g., the most likely pronunciation).

When a speech input is received, STT processing module 730 is used to determine the phonemes corresponding to the speech input (e.g., using an acoustic model), and then attempt to determine words that match the phonemes (e.g., using a language model). For example, if STT processing module 730 first identifies the sequence of phonemes /

/ corresponding to a portion of the speech input, it can then determine, based on vocabulary index 744, that this sequence corresponds to the word “tomato.”

In some examples, STT processing module 730 uses approximate matching techniques to determine words in an utterance. Thus, for example, the STT processing module 730 determines that the sequence of phonemes /

/ corresponds to the word “tomato,” even if that particular sequence of phonemes is not one of the candidate sequence of phonemes for that word.

Natural language processing module 732 (“natural language processor”) of the digital assistant takes the n-best candidate text representation(s) (“word sequence(s)” or “token sequence(s)”) generated by STT processing module 730, and attempts to associate each of the candidate text representations with one or more “actionable intents” recognized by the digital assistant. An “actionable intent” (or “user intent”) represents a task that can be performed by the digital assistant, and can have an associated task flow implemented in task flow models 754. The associated task flow is a series of programmed actions and steps that the digital assistant takes in order to perform the task. The scope of a digital assistant's capabilities is dependent on the number and variety of task flows that have been implemented and stored in task flow models 754, or in other words, on the number and variety of “actionable intents” that the digital assistant recognizes. The effectiveness of the digital assistant, however, also dependents on the assistant's ability to infer the correct “actionable intent(s)” from the user request expressed in natural language.

In some examples, in addition to the sequence of words or tokens obtained from STT processing module 730, natural language processing module 732 also receives contextual information associated with the user request, e.g., from I/O processing module 728. The natural language processing module 732 optionally uses the contextual information to clarify, supplement, and/or further define the information contained in the candidate text representations received from STT processing module 730. The contextual information includes, for example, user preferences, hardware, and/or software states of the user device, sensor information collected before, during, or shortly after the user request, prior interactions (e.g., dialogue) between the digital assistant and the user, and the like. As described herein, contextual information is, in some examples, dynamic, and changes with time, location, content of the dialogue, and other factors.

In some examples, the natural language processing is based on, e.g., ontology 760. Ontology 760 is a hierarchical structure containing many nodes, each node representing either an “actionable intent” or a “property” relevant to one or more of the “actionable intents” or other “properties.” As noted above, an “actionable intent” represents a task that the digital assistant is capable of performing, i.e., it is “actionable” or can be acted on. A “property” represents a parameter associated with an actionable intent or a sub-aspect of another property. A linkage between an actionable intent node and a property node in ontology 760 defines how a parameter represented by the property node pertains to the task represented by the actionable intent node.

In some examples, ontology 760 is made up of actionable intent nodes and property nodes. Within ontology 760, each actionable intent node is linked to one or more property nodes either directly or through one or more intermediate property nodes. Similarly, each property node is linked to one or more actionable intent nodes either directly or through one or more intermediate property nodes. For example, as shown in FIG. 7C, ontology 760 includes a “restaurant reservation” node (i.e., an actionable intent node). Property nodes “restaurant,” “date/time” (for the reservation), and “party size” are each directly linked to the actionable intent node (i.e., the “restaurant reservation” node).

In addition, property nodes “cuisine,” “price range,” “phone number,” and “location” are sub-nodes of the property node “restaurant,” and are each linked to the “restaurant reservation” node (i.e., the actionable intent node) through the intermediate property node “restaurant.” For another example, as shown in FIG. 7C, ontology 760 also includes a “set reminder” node (i.e., another actionable intent node). Property nodes “date/time” (for setting the reminder) and “subject” (for the reminder) are each linked to the “set reminder” node. Since the property “date/time” is relevant to both the task of making a restaurant reservation and the task of setting a reminder, the property node “date/time” is linked to both the “restaurant reservation” node and the “set reminder” node in ontology 760.

An actionable intent node, along with its linked property nodes, is described as a “domain.” In the present discussion, each domain is associated with a respective actionable intent, and refers to the group of nodes (and the relationships there between) associated with the particular actionable intent. For example, ontology 760 shown in FIG. 7C includes an example of restaurant reservation domain 762 and an example of reminder domain 764 within ontology 760. The restaurant reservation domain includes the actionable intent node “restaurant reservation,” property nodes “restaurant,” “date/time,” and “party size,” and sub-property nodes “cuisine,” “price range,” “phone number,” and “location.” Reminder domain 764 includes the actionable intent node “set reminder,” and property nodes “subject” and “date/time.” In some examples, ontology 760 is made up of many domains. Each domain shares one or more property nodes with one or more other domains. For example, the “date/time” property node is associated with many different domains (e.g., a scheduling domain, a travel reservation domain, a movie ticket domain, etc.), in addition to restaurant reservation domain 762 and reminder domain 764.

While FIG. 7C illustrates two example domains within ontology 760, other domains include, for example, “find a movie,” “initiate a phone call,” “find directions,” “schedule a meeting,” “send a message,” and “provide an answer to a question,” “read a list,” “providing navigation instructions,” “provide instructions for a task” and so on. A “send a message” domain is associated with a “send a message” actionable intent node, and further includes property nodes such as “recipient(s),” “message type,” and “message body.” The property node “recipient” is further defined, for example, by the sub-property nodes such as “recipient name” and “message address.”

In some examples, ontology 760 includes all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some examples, ontology 760 is modified, such as by adding or removing entire domains or nodes, or by modifying relationships between the nodes within the ontology 760.

In some examples, nodes associated with multiple related actionable intents are clustered under a “super domain” in ontology 760. For example, a “travel” super-domain includes a cluster of property nodes and actionable intent nodes related to travel. The actionable intent nodes related to travel includes “airline reservation,” “hotel reservation,” “car rental,” “get directions,” “find points of interest,” and so on. The actionable intent nodes under the same super domain (e.g., the “travel” super domain) have many property nodes in common. For example, the actionable intent nodes for “airline reservation,” “hotel reservation,” “car rental,” “get directions,” and “find points of interest” share one or more of the property nodes “start location,” “destination,” “departure date/time,” “arrival date/time,” and “party size.”

In some examples, each node in ontology 760 is associated with a set of words and/or phrases that are relevant to the property or actionable intent represented by the node. The respective set of words and/or phrases associated with each node are the so-called “vocabulary” associated with the node. The respective set of words and/or phrases associated with each node are stored in vocabulary index 744 in association with the property or actionable intent represented by the node. For example, returning to FIG. 7B, the vocabulary associated with the node for the property of “restaurant” includes words such as “food,” “drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” and so on. For another example, the vocabulary associated with the node for the actionable intent of “initiate a phone call” includes words and phrases such as “call,” “phone,” “dial,” “ring,” “call this number,” “make a call to,” and so on. The vocabulary index 744 optionally includes words and phrases in different languages.

Natural language processing module 732 receives the candidate text representations (e.g., text string(s) or token sequence(s)) from STT processing module 730, and for each candidate representation, determines what nodes are implicated by the words in the candidate text representation. In some examples, if a word or phrase in the candidate text representation is found to be associated with one or more nodes in ontology 760 (via vocabulary index 744), the word or phrase “triggers” or “activates” those nodes. Based on the quantity and/or relative importance of the activated nodes, natural language processing module 732 selects one of the actionable intents as the task that the user intended the digital assistant to perform. In some examples, the domain that has the most “triggered” nodes is selected. In some examples, the domain having the highest confidence value (e.g., based on the relative importance of its various triggered nodes) is selected. In some examples, the domain is selected based on a combination of the number and the importance of the triggered nodes. In some examples, additional factors are considered in selecting the node as well, such as whether the digital assistant has previously correctly interpreted a similar request from a user.

User data 748 includes user-specific information, such as user-specific vocabulary, user preferences, user address, user's default and secondary languages, user's contact list, and other short-term or long-term information for each user. In some examples, natural language processing module 732 uses the user-specific information to supplement the information contained in the user input to further define the user intent. For example, for a user request “invite my friends to my birthday party,” natural language processing module 732 is able to access user data 748 to determine who the “friends” are and when and where the “birthday party” would be held, rather than requiring the user to provide such information explicitly in his/her request.

It should be recognized that in some examples, natural language processing module 732 is implemented using one or more machine learning mechanisms (e.g., neural networks). In particular, the one or more machine learning mechanisms are configured to receive a candidate text representation and contextual information associated with the candidate text representation. Based on the candidate text representation and the associated contextual information, the one or more machine learning mechanisms are configured to determine intent confidence scores over a set of candidate actionable intents. Natural language processing module 732 can select one or more candidate actionable intents from the set of candidate actionable intents based on the determined intent confidence scores. In some examples, an ontology (e.g., ontology 760) is also used to select the one or more candidate actionable intents from the set of candidate actionable intents.

Other details of searching an ontology based on a token string are described in U.S. Utility application Ser. No. 12/341,743 for “Method and Apparatus for Searching Using An Active Ontology,” filed Dec. 22, 2008, the entire disclosure of which is incorporated herein by reference.

In some examples, once natural language processing module 732 identifies an actionable intent (or domain) based on the user request, natural language processing module 732 generates a structured query to represent the identified actionable intent. In some examples, the structured query includes parameters for one or more nodes within the domain for the actionable intent, and at least some of the parameters are populated with the specific information and requirements specified in the user request. For example, the user says “Make me a dinner reservation at a sushi place at 7.” In this case, natural language processing module 732 is able to correctly identify the actionable intent to be “restaurant reservation” based on the user input. According to the ontology, a structured query for a “restaurant reservation” domain includes parameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and the like. In some examples, based on the speech input and the text derived from the speech input using STT processing module 730, natural language processing module 732 generates a partial structured query for the restaurant reservation domain, where the partial structured query includes the parameters {Cuisine=“Sushi” } and {Time=“7 pm” }. However, in this example, the user's utterance contains insufficient information to complete the structured query associated with the domain. Therefore, other necessary parameters such as {Party Size} and {Date} are not specified in the structured query based on the information currently available. In some examples, natural language processing module 732 populates some parameters of the structured query with received contextual information. For example, in some examples, if the user requested a sushi restaurant “near me,” natural language processing module 732 populates a {location} parameter in the structured query with GPS coordinates from the user device.

In some examples, natural language processing module 732 identifies multiple candidate actionable intents for each candidate text representation received from STT processing module 730. Further, in some examples, a respective structured query (partial or complete) is generated for each identified candidate actionable intent. Natural language processing module 732 determines an intent confidence score for each candidate actionable intent and ranks the candidate actionable intents based on the intent confidence scores. In some examples, natural language processing module 732 passes the generated structured query (or queries), including any completed parameters, to task flow processing module 736 (“task flow processor”). In some examples, the structured query (or queries) for the m-best (e.g., m highest ranked) candidate actionable intents are provided to task flow processing module 736, where m is a predetermined integer greater than zero. In some examples, the structured query (or queries) for the m-best candidate actionable intents are provided to task flow processing module 736 with the corresponding candidate text representation(s).

Other details of inferring a user intent based on multiple candidate actionable intents determined from multiple candidate text representations of a speech input are described in U.S. Utility application Ser. No. 14/298,725 for “System and Method for Inferring User Intent From Speech Inputs,” filed Jun. 6, 2014, the entire disclosure of which is incorporated herein by reference.

Task flow processing module 736 is configured to receive the structured query (or queries) from natural language processing module 732, complete the structured query, if necessary, and perform the actions required to “complete” the user's ultimate request. In some examples, the various procedures necessary to complete these tasks are provided in task flow models 754. In some examples, task flow models 754 include procedures for obtaining additional information from the user and task flows for performing actions associated with the actionable intent.

As described above, in order to complete a structured query, task flow processing module 736 needs to initiate additional dialogue with the user in order to obtain additional information, and/or disambiguate potentially ambiguous utterances. When such interactions are necessary, task flow processing module 736 invokes dialogue flow processing module 734 to engage in a dialogue with the user. In some examples, dialogue flow processing module 734 determines how (and/or when) to ask the user for the additional information and receives and processes the user responses. The questions are provided to and answers are received from the users through I/O processing module 728. In some examples, dialogue flow processing module 734 presents dialogue output to the user via audio and/or visual output, and receives input from the user via spoken or physical (e.g., clicking) responses. Continuing with the example above, when task flow processing module 736 invokes dialogue flow processing module 734 to determine the “party size” and “date” information for the structured query associated with the domain “restaurant reservation,” dialogue flow processing module 734 generates questions such as “For how many people?” and “On which day?” to pass to the user. Once answers are received from the user, dialogue flow processing module 734 then populates the structured query with the missing information, or pass the information to task flow processing module 736 to complete the missing information from the structured query.

Once task flow processing module 736 has completed the structured query for an actionable intent, task flow processing module 736 proceeds to perform the ultimate task associated with the actionable intent. Accordingly, task flow processing module 736 executes the steps and instructions in the task flow model according to the specific parameters contained in the structured query. For example, the task flow model for the actionable intent of “restaurant reservation” includes steps and instructions for contacting a restaurant and actually requesting a reservation for a particular party size at a particular time. For example, using a structured query such as: {restaurant reservation, restaurant=ABC Café, date=3/12/2012, time=7 pm, party size=5}, task flow processing module 736 performs the steps of: (1) logging onto a server of the ABC Café or a restaurant reservation system such as OPENTABLE®, (2) entering the date, time, and party size information in a form on the website, (3) submitting the form, and (4) making a calendar entry for the reservation in the user's calendar.

In some examples, task flow processing module 736 employs the assistance of service processing module 738 (“service processing module”) to complete a task requested in the user input or to provide an informational answer requested in the user input. For example, service processing module 738 acts on behalf of task flow processing module 736 to make a phone call, set a calendar entry, invoke a map search, invoke or interact with other user applications installed on the user device, and invoke or interact with third-party services (e.g., a restaurant reservation portal, a social networking website, a banking portal, etc.). In some examples, the protocols and application programming interfaces (API) required by each service are specified by a respective service model among service models 756. Service processing module 738 accesses the appropriate service model for a service and generates requests for the service in accordance with the protocols and APIs required by the service according to the service model.

For example, if a restaurant has enabled an online reservation service, the restaurant submits a service model specifying the necessary parameters for making a reservation and the APIs for communicating the values of the necessary parameter to the online reservation service. When requested by task flow processing module 736, service processing module 738 establishes a network connection with the online reservation service using the web address stored in the service model, and sends the necessary parameters of the reservation (e.g., time, date, party size) to the online reservation interface in a format according to the API of the online reservation service.

In some examples, natural language processing module 732, dialogue flow processing module 734, and task flow processing module 736 are used collectively and iteratively to infer and define the user's intent, obtain information to further clarify and refine the user intent, and finally generate a response (i.e., an output to the user, or the completion of a task) to fulfill the user's intent. The generated response is a dialogue response to the speech input that at least partially fulfills the user's intent. Further, in some examples, the generated response is output as a speech output. In these examples, the generated response is sent to speech synthesis processing module 740 (e.g., speech synthesizer) where it can be processed to synthesize the dialogue response in speech form. In yet other examples, the generated response is data content relevant to satisfying a user request in the speech input.

In examples where task flow processing module 736 receives multiple structured queries from natural language processing module 732, task flow processing module 736 initially processes the first structured query of the received structured queries to attempt to complete the first structured query and/or execute one or more tasks or actions represented by the first structured query. In some examples, the first structured query corresponds to the highest ranked actionable intent. In other examples, the first structured query is selected from the received structured queries based on a combination of the corresponding speech recognition confidence scores and the corresponding intent confidence scores. In some examples, if task flow processing module 736 encounters an error during processing of the first structured query (e.g., due to an inability to determine a necessary parameter), the task flow processing module 736 can proceed to select and process a second structured query of the received structured queries that corresponds to a lower ranked actionable intent. The second structured query is selected, for example, based on the speech recognition confidence score of the corresponding candidate text representation, the intent confidence score of the corresponding candidate actionable intent, a missing necessary parameter in the first structured query, or any combination thereof.

Speech synthesis processing module 740 is configured to synthesize speech outputs for presentation to the user. Speech synthesis processing module 740 synthesizes speech outputs based on text provided by the digital assistant. For example, the generated dialogue response is in the form of a text string. Speech synthesis processing module 740 converts the text string to an audible speech output. Speech synthesis processing module 740 uses any appropriate speech synthesis technique in order to generate speech outputs from text, including, but not limited, to concatenative synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, articulatory synthesis, hidden Markov model (HMM) based synthesis, and sinewave synthesis. In some examples, speech synthesis processing module 740 is configured to synthesize individual words based on phonemic strings corresponding to the words. For example, a phonemic string is associated with a word in the generated dialogue response. The phonemic string is stored in metadata associated with the word. Speech synthesis processing module 740 is configured to directly process the phonemic string in the metadata to synthesize the word in speech form.

In some examples, instead of (or in addition to) using speech synthesis processing module 740, speech synthesis is performed on a remote device (e.g., the server system 108), and the synthesized speech is sent to the user device for output to the user. For example, this can occur in some implementations where outputs for a digital assistant are generated at a server system. And because server systems generally have more processing power or resources than a user device, it is possible to obtain higher quality speech outputs than would be practical with client-side synthesis.

Additional details on digital assistants can be found in the U.S. Utility application Ser. No. 12/987,982, entitled “Intelligent Automated Assistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No. 13/251,088, entitled “Generating and Processing Task Items That Represent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosures of which are incorporated herein by reference.

4. Exemplary Functions and Architectures of a Digital Assistant Providing Improved Detection and Correction of Grammatical Errors.

FIG. 8 illustrates a block diagram of a digital assistant 800 for generating a grammatical error correction model, according to various examples. In some examples, digital assistant 800 (e.g., digital assistant system 700) is implemented by a user device according to various examples. In some examples, the user device, a server (e.g., server 108), or a combination thereof, can implement digital assistant 800. The user device can be implemented using, for example, device 104, 200, 400, 600, 1100, or 1300 as illustrated in FIGS. 1, 2A-2B, 4, 6A-6B, 11, and 13A-13B. In some examples, digital assistant 800 can be implemented using digital assistant module 726 of digital assistant system 700. Digital assistant 800 includes one or more modules, models, applications, vocabularies, and user data similar to those of digital assistant module 726. For example, digital assistant 800 includes the following sub-modules, or a subset or superset thereof: an input/output processing module, an STT process module, a natural language processing module, a task flow processing module, and a speech synthesis module. These modules can also be implemented similar to that of the corresponding modules as illustrated in FIG. 7B, and therefore are not shown and not repeatedly described.

As illustrated in FIG. 8, in some examples, digital assistant 800 includes neural network 810 which further includes generator 820, reconstructor 830, and discriminator 840. As illustrated in FIG. 8, digital assistant 800 can receive a set of input words 802, which may or may not include grammatical errors. In some examples, a grammatical error includes a homophone error, an apostrophe error, a subject and verb error, a tense error, or any other type of grammatical error. A homophone error includes when two words have the same pronunciation, but different meanings or spellings, and the incorrect word was used (e.g., “too” instead of “to” or “their” instead of “there” or “they're”). An apostrophe error includes when an apostrophe is placed in a word incorrectly or is used in the incorrect part of the word and thus changes the meaning of the word (e.g., “its” instead of “it's,” “taxi's” instead of “taxis,” “Joes” instead of “Joe's,” etc.). A subject and verb error includes when the incorrect type of verb is used with a particular subject (e.g., when a plural verb is used with a singular subject, such as “the list of items are on the desk” instead of “the list of items is on the desk”). A tense error includes when the incorrect tense of word is used in a sentence (e.g., when the present tense “going” is used instead of the past tense “gone”). Grammatical errors may also include the incorrect placement of words in a sentence, an incorrect word used in a sentence, incorrect capitalization, incorrect punctuation, etc.

In some examples, the set of input words 802 can be provided by a user, by a computer, and/or by an application (e.g., a dictionary application, a message application). The set of input words 802 can be provided by one particular user, by multiple different users, or by a group of users. The set of input words 802 can be customized (e.g., for a particular user, a group of users, etc.) to include sets of words commonly used by that specific user or group of users. The set of input words 802 can also be customized to include sets of words commonly used by a general population. Further, the set of input words 802 can be customized to add sets of words associated with a specific user or group of users, even if the sets of words are not commonly or frequently used.

In some examples, the set of input words 802 can include one or more sentences, a plurality of words of one or more sentences, a paragraph, or any other structure that provides context about the input words. In some examples, the set of input words 802 can include sets of words (e.g., sentences) that are absent grammatical errors and sets of words that include grammatical errors.

As illustrated in FIG. 8, digital assistant 800 can receive a reference set of words 804, which does not include grammatical errors. In some examples, the reference set of words 804 can be provided by a user, by a computer, and/or by an application (e.g., a dictionary application, a message application). The reference set of words 804 can be provided by one particular user, by multiple different users, or by a group of users. The reference set of words 804 can be customized (e.g., for a particular user, a group of users, etc.) to include sets of words commonly used by that specific user or group of users. The reference set of words 804 can also be customized to include sets of words commonly used by a general population. Further, the reference set of words 804 can be customized to add sets of words associated with a specific user or group of users, even if the sets of words are not commonly or frequently used. In some examples, the reference set of words 804 can be provided by one or more style guides which include sets of grammatically correct words. In some examples, the reference set of words 804 provided by a user, computer, or an application can be compared to a style guide to determine that the reference set of words 804 does not include grammatical errors. In some examples, when the reference set of words 804 is determined to include grammatical errors the reference set of words 804 may be discarded by digital assistant 800.

In some examples, the reference set of words 804 can include one or more sentences, a plurality of words of one or more sentences, a paragraph, or any other structure that provides context about the input words. In some examples, the reference set of words 804 includes a version of the set of inputs words 802 without any grammatical errors. For examples, the set of input words 802 may include a sentence like “I'm going shopping two,” and the reference set of words 804 may include the sentence “I'm going shopping too,” which corrects the grammatical error of using “to” instead of “too” in the set of input words 802. In some examples, the reference set of words 804 can be selected based on the grammatical errors that are included in the set of input words 802. For example, if the current set of input words 802 includes homophone errors the reference set of words 804 can be selected to include sets of words (e.g., sentences) that correct homophone errors. As another example, if the current set of input words 802 includes apostrophe errors the reference set of words 804 can be selected to include sets of words (e.g., sentences) that correct apostrophe errors. In some examples, the reference set of words 804 can include sets of words (e.g., sentences) that correct multiple types of grammatical errors.

FIG. 9 illustrates a block diagram of a neural network 810 for generating grammatically correct sets of words, according to various examples. In some examples, neural network 810 includes generator 820, reconstructor 830, and discriminator 840. As illustrated in FIG. 9, neural network 810 can generate grammatical error correction model 842 based on the set of input words 802. In some examples, neural network 810 includes a generative adversarial network (GAN). A generative adversarial network is type of learning network used in unsupervised machine learning and can include a generator 820 and a discriminator 840 contesting each other in a zero-sum game framework. In particular, neural network 810 is trained to generate one or more output sets of words (e.g., sentences) that include grammatical errors based on the set of input words 802 and the reference set of words 804. As described in more detail below, the output sets of words determined by neural network 810 can have a probability distribution corresponding to a probability distribution of the reference sets of words 804, which is a collection of sets of words that do not have grammatical errors.

As illustrated by FIG. 9, in some examples, generator 820 receives the set of input words 802 (e.g., denoted by x). In some examples, the set of input words 802 includes one or more sentences represented by a fixed-length vector (e.g., a vector denoted by x generated from one or more input sets of words and/or one or more perturbed vectors or set of input words). In some examples, the fixed-length vector representing the set of input words 802 has a dimension equal to N.

In some examples, generator 820 receives a reference set of words 804 (e.g., denoted by w) in addition to the set of input words 802. In some examples, the reference set of words 804 includes one or more sentences represented by a fixed-length vector (e.g., a vector denoted by w generated from one or more reference sets of words and/or one or more perturbed vectors or reference set of words). In some examples, the fixed-length vector representing the reference set of words 804 has a dimension equal to N.

In some examples, generator 820 can include a bidirectional LSTM, which is a type of RNN. A bidirectional LSTM network can also include an input layer, one or more hidden layers, and an output layer. An input layer in the bidirectional LSTM receives an input vector; the one or more hidden layers provide one or more state vectors for factoring in contextual data; and the output layer generates output vectors using the state vectors. A state vector in a bidirectional LSTM includes a preceding context of a current time step or a following context of the current time step. In some examples, a preceding context is also referred to as a left context of a current word for a current time step. In some examples, a following context is also referred to as a right context of a current word for a current time step.

In some examples, the bidirectional LSTM of generator 820 encodes a sentence through grammaticality transfer. In some examples, the grammaticality transfer is encoded using a first bidirectional LSTM layer of the bidirectional LSTM. In some examples, each individual word of a vector generated by the bidirectional LSTM of generator 820 is represented using 1-of-N encoding, where N is the size of the underlying vocabulary considered (e.g., the size of the input vectors x and w).

In some examples, generator 820 generates a plurality of first interim vectors (e.g., denoted by s_(l) and r_(l)) encoded with the contextual data associated with the set of input words 802. In some examples, to generate the first interim vectors, generator 820 determines, for a current word of the set of input words 802 (e.g., represented by a vector denoted by x_(l)), a first interim vector 910 (e.g., denoted by s_(l)) representing the preceding context of the current character. The first interim vector 910 (e.g., denoted by s_(l)) can be determined based on formula (1) below.

s _(l) =T{W _(SX) ·x _(l) +W _(SS) ·s _(l−1)}  (1)

where the first interim vector 910 (e.g., denoted by s_(k)) is determined based on a preceding interim vector (e.g., denoted by s_(l−1)) representing the preceding context at a preceding time step and a vector representing a current word of the set of input words 802 (e.g., x_(l)). In formula (1), x_(l) denotes a vector representing a current word of the set of input words 802 for a current time step; s_(l−1) denotes a preceding interim vector representing the preceding context at a preceding time step; and W_(SX) and W_(SS) denote weight matrices of compatible dimensions. In some examples, W_(SX) and W_(SS) can be updated during the training of the LSTM network in generator 920. In formula (1), T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof.

In some examples, a preceding interim vector (e.g., s_(l−1)) is an internal representation of context from one or more output values of a preceding time step (e.g., a past time step) associated with hidden nodes in the hidden layer of the LSTM network in generator 920. In some examples, a preceding interim vector (e.g., denoted by s_(l−1)) has a dimension of H. In some examples, a preceding interim vector may be a dense vector. The first interim vector 910 (e.g., denoted by s_(l)) representing the preceding context is an internal representation of preceding context as an output of a current time step of the LSTM network in generator 920 (e.g., output values of preceding hidden nodes of the hidden layer of an LSTM network in generator 920 at a current time step).

In some examples, generator 920 also determines, for a current word of the set of input words 802 (e.g., represented by a vector denoted by x_(l)), another first interim vector 912 (e.g., denoted by r_(l)) representing the following context of the current character. The first interim vector 912 (e.g., denoted by r_(l)) can be determined based on formula (2) below.

r _(l) =T{W _(RX) ·x _(l) +W _(RR) ·r _(l+1)}  (2)

where the first interim vector 912 (e.g., denoted by r_(l)) is determined based on a following first interim vector (e.g., denoted by r_(l+1)) representing the following context at a following time step and a vector representing a current word of the set of input words 802 (e.g., x_(l)). In formula (2), x_(l) denotes a vector representing a current word of the set of input words 802 for a current time step; r_(l+1) denotes a following interim vector representing the following context at a following time step (a future time step); and W_(RX) and W_(RR) denote weight matrices of compatible dimensions. In some examples, W_(RX) and W_(RR) can be updated during the training of the LSTM network in generator 920. In formula (2), T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof.

In some examples, a following interim vector (e.g., denoted by r_(l+1)) is an internal representation of context from one or more output values of a following time step (e.g., a future time step) associated with hidden nodes in the hidden layer of the LSTM network in generator 920. A following interim vector (e.g., denoted by r_(l+1)) can have a dimension of H. In some examples, a following interim vector may be a dense vector. The first interim vector 912 (e.g., denoted by r_(l)) representing the following context is an internal representation of following context as an output of a current time step of the LSTM network in generator 920 (e.g., the output values of following hidden nodes of the hidden layer of an LSTM network in generator 920 at a current time step).

In some examples, the encoded contextual information represents one or more words preceding or following the current word of the set of input words 802 (e.g., represented by the vector denoted by x_(l)). In some examples, the encoded contextual information represents one or more sentences preceding or following the current word of the set of input words 802 (e.g., represented by the vector denoted by x_(l)).

In some examples, generator 820 generates another plurality of first interim vectors (e.g., denoted by s_(k) and r_(k)) encoded with the contextual data associated with the reference set of words 804. In some examples, to generate the interim vectors, generator 820 determines, for a current word of the reference set of words 804 (e.g., represented by a vector denoted by w_(k)), a first interim vector 914 (e.g., denoted by s_(k)) representing the preceding context of the current character. The first interim vector 914 (e.g., denoted by s_(k)) can be determined based on formula (3) below.

s _(k) =T{W _(SX) ·w _(k) +W _(SS) ·s _(k−1)}  (3)

where the first interim vector 914 (e.g., denoted by s_(k)) is determined based on a preceding interim vector (e.g., denoted by s_(k−1)) representing the preceding context at a preceding time step and a vector representing a current word of the reference set of words 804 (e.g., w_(k)). In formula (3), w_(k) denotes a vector representing a current word of the reference set of words 804 for a current time step; s_(k−1) denotes a preceding interim vector representing the preceding context at a preceding time step; and W_(SX) and W_(SS) denote weight matrices of compatible dimensions. In some examples, W_(SX) and W_(SS) can be updated during the training of the LSTM network in generator 920. In formula (3), T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof.

In some examples, a preceding interim vector (e.g., s_(k−1)) is an internal representation of context from one or more output values of a preceding time step (e.g., a past time step) associated with hidden nodes in the hidden layer of the LSTM network in generator 920. In some examples, a preceding interim vector (e.g., denoted by s_(k−1)) has a dimension of H. In some examples, a preceding interim vector may be a dense vector. The first interim vector 914 (e.g., denoted by s_(k)) representing the preceding context is an internal representation of preceding context as an output of a current time step of the LSTM network in generator 920 (e.g., output values of preceding hidden nodes of the hidden layer of an LSTM network in generator 920 at a current time step).

In some examples, generator 920 also determines, for a current word of the reference set of words 804 (e.g., represented by a vector denoted by w_(k)), another first interim vector 916 (e.g., denoted by r_(k)) representing the following context of the current character. The first interim vector 916 (e.g., denoted by r_(k)) can be determined based on formula (4) below.

r _(k) =T{W _(RX) ·w _(k) +W _(RR) ·r _(k+1)}  (4)

where the first interim vector 916 (e.g., denoted by r_(k)) is determined based on a following first interim vector (e.g., denoted by r_(k+1)) representing the following context at a following time step and a vector representing a current word of the reference set of words 804 (e.g., w_(k)). In formula (4), w_(k) denotes a vector representing a current word of the set of input words 802 for a current time step; r_(k+1) denotes a following interim vector representing the following context at a following time step (a future time step); and W_(RX) and W_(RR) denote weight matrices of compatible dimensions. In some examples, W_(RX) and W_(RR) can be updated during the training of the LSTM network in generator 920. In formula (4), T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof.

In some examples, a following interim vector (e.g., denoted by r_(k+1)) is an internal representation of context from one or more output values of a following time step (e.g., a future time step) associated with hidden nodes in the hidden layer of the LSTM network in generator 920. A following interim vector (e.g., denoted by r_(k+1)) can have a dimension of H. In some examples, a following interim vector may be a dense vector. The first interim vector 916 (e.g., denoted by r_(k)) representing the following context is an internal representation of following context as an output of a current time step of the LSTM network in generator 920 (e.g., the output values of following hidden nodes of the hidden layer of an LSTM network in generator 920 at a current time step).

In some examples, the encoded contextual information represents one or more words preceding or following the current word of the reference set of words 804 (e.g., represented by the vector denoted by w_(k)). In some examples, the encoded contextual information represents one or more sentences preceding or following the current word of the reference set of words 804 (e.g., represented by the vector denoted by w_(k)).

In some examples, generator 820 also generates a vector representing a transformed set of words 822 (e.g., denoted by y_(l′)). In some examples, the transformed set of words 822 is an attempt by the generator to correct the grammatical errors present in the set of input words 802 via grammaticality transfer. In some examples, the transformed set of words 822 is generated using the bidirectional LSTM network in generator 920 and based on the first interim vectors 910 and 912 discussed above (e.g., denoted by s_(l), and r_(l)). In some examples, generator 920 applies the correct grammar of the reference set of words 804 to the set of input words 802 to generate the transformed set of words 822. For example, when the set of input words 802 includes a homophone error like using “too” instead of “to,” generator 920 may apply the grammaticality of a reference set of words from the reference set of words 804 that has a similar error to attempt to correct this error. Thus, the transformed set of words 822 may replace “too” with a word that generator 920 believes is the correct homophone. In some examples, generator 920 is trained iteratively with the other portions of the system, changing the transformed set of words 822 with each iteration. Accordingly, generator 920 may determine different sets of transformed set of words 822 based on the same input words 802 and reference words 804 as the system is trained.

In some examples, generator 820 generates, based on the current vector representing the preceding context (e.g., denoted by s_(l)) and the current vector representing the following context (e.g., denoted by r_(l)), one or more vectors representing the transformed set of words 822 (e.g., denoted by y_(l′)). As discussed above, the transformed set of words represents a set of words (e.g., a sentence) with an attempted corrected grammatical error. In some examples, the vector representing the transformed set of words 822 (e.g., denoted by y_(l′)) can be generated using the formula (5) below.

y _(l′) =S{W _(YS) ·s _(l) +W _(YR′) ·r _(l)}  (5)

where, s_(l) represents a state vector representing the preceding context at a current time step; r_(l) represents a state vector representing the following context at a current time step; and W_(YS) and W_(YR) represent weight matrices with compatible dimensions. W_(YS) and W_(YR) can be updated during unsupervised training of neural network 810. In some examples, S{ } denotes a softmax activation function. In some examples, the vector representing the transformed set of words 822 (e.g, denoted by y_(l′)) has a 1-of-N encoding (e.g., similar to the encoding of characters in the set of input words 802) and thus has a dimension of N.

As shown in FIG. 9, in some examples, the interim vectors generated by generator 820 (e.g., s_(k), and r_(k)) are provided to reconstructor 830 for further processing. FIG. 10 illustrates a block diagram of a reference set of words reconstructor 830 according to various examples.

As shown in FIG. 10, in some examples, reconstructor 830 receives the interim vectors generated by generator 820 including first interim vectors 914 and 916 (denoted by s_(k), and r_(k) respectively). In some examples, reconstructor 830 includes encoder 1010 and decoder 1020. In some examples, as shown in FIG. 10, encoder 1010 includes first aggregating layer 1030, extracting layer 1040 and second aggregating layer 1050. In some examples, first aggregating layer 1030 aggregates first interim vectors 914 and 916 (e.g., denoted by s₁, s₂ . . . s_(k), s_(k+1) . . . s_(K+1) and r₁, r₂, . . . r_(k), r_(k+1) . . . r_(K+1)). In some examples, aggregating first interim vectors 914 and 916 includes an average pooling function/operation?. Average pooling performs, for example, down-sampling by dividing the interim vectors into pooling regions and computing the average values of each region. Average pooling in this way increases the effective modeling span by distilling large sets of data into a finite vector. In some examples, aggregating the interim vectors can include other types of pooling, such as maximum pooling.

With reference to FIG. 10, in some examples, first aggregating layer 1030 can include a forward average pooling stage and a backward average pooling stage. In some examples, aggregating the interim vectors includes an average pooling, a maximum pooling, or any other pooling algorithms. In the forward average pooling stage, one or more subsets of the interim vectors (e.g., first interim vector 914 denoted by s₁, s₂ . . . s_(k), s_(k+1) . . . s_(K+1)) representing the preceding context are aggregated to generate a plurality of second interim vectors (e.g., a second interim vector 1032 denoted by f_(p)). In some examples, the forward average pooling stage generates a plurality of second interim vectors including a second interim vector 1032 (e.g., f_(p)) according to formula (6) below.

$\begin{matrix} {f_{p} = {\frac{1}{I_{p}}{\sum\limits_{k\; {\epsilon I}_{P}}s_{k}}}} & (6) \end{matrix}$

where, as described with respect to formula (3) above, s_(k) denotes an interim vector representing the preceding context of the current character; and I_(p) denotes the pth instance of P non-overlapping subsets of [1 . . . K], each associated with a span of approximately [K/P] characters.

With reference to FIG. 10, in some examples, first aggregating layer 1030 can also include, for example, a backward average pooling stage. In the backward average pooling stage, one or more subsets of the interim vectors (e.g., second interim vector 916 denoted by r₁, r₂, . . . r_(k), r_(k+1), . . . r_(K)) representing the following context are aggregated to generate a plurality of second interim vectors (e.g., second interim vector 1034 denoted by b_(p)). In some examples, the backward average pooling layer generates a plurality of second interim vectors including a second interim vector 1034 (e.g., b_(p)) according to formula (7) below.

$\begin{matrix} {b_{p} = {\frac{1}{I_{p}}{\sum\limits_{k\; \epsilon \; I_{P}}r_{k}}}} & (7) \end{matrix}$

where, as described with respect to formula (4) above, r_(k) denotes an interim vector representing the following context of the current character; and I_(p) denotes the pth instance of P non-overlapping subsets of [1 . . . K], each associated with a span of approximately [K/P] characters. In some examples, because of the pooling operations, the total number of second interim vectors (e.g., vectors 1032 and 1034) is less than that of the first interim vectors (e.g., vectors 914 and 916).

As illustrated in FIG. 10, in some examples, the second interim vectors (e.g., vectors 1032 and 1034) are then provided to a second stage of extracting and aggregating, which can include extracting layer 1030 and second aggregating layer 1040. In some examples, as a result of the aggregating operation performed in generator 820 and the first stage of aggregating, the second stage of extracting and aggregating include less number of RNN cells or elements in extracting layer 1030 and less number of pooling elements in second aggregating layer 1040. In some examples, the plurality of second interim vectors (e.g., vectors 1032 and 1034) are provided to extracting layer 1030. Extracting layer 1030 can also include a RNN. In particular, extracting layer 1030 can include, for example, a bi-direction LSTM with two versions of context: preceding context and following context.

As shown in FIG. 10, extracting layer 1030 generates a plurality of third interim vectors based on the plurality of second interim vectors. In some examples, to generate the third interim vectors, extracting layer 1030 determines, for a second interim vector 1032 (e.g., denoted by f_(p)), a third interim vector 1042 (e.g., denoted by u_(p)) representing the preceding context of the second interim vector 1032. Third interim vector 1042 (e.g., denoted by u_(p)) can be determined according to formula (8) below.

u _(p) =T{W _(UF) ·f _(p) +W _(UU) ·u _(p−1)}  (8)

where third interim vector 1042 (e.g., denoted by u_(p)) is determined based on a preceding third interim vector (e.g., denoted by u_(p−1)) representing the preceding context at a preceding time step and a second interim vector 1032 (e.g., f_(p)). In formula (8), f_(p) denotes second interim vector 1032 at a current time step; u_(p−1) denotes a preceding third interim vector representing the preceding context at a previous time step (a past time step); and W_(UF) and W_(UU) denote weight matrices of compatible dimensions. In some examples, W_(UF) and W_(UU) can be updated during the training of the LSTM network in extracting layer 1030. In formula (8), T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof.

In some examples, a preceding third interim vector (e.g., denoted by u_(p−1)) is an internal representation of context from one or more output values of a preceding time step (e.g., a past time step) associated with hidden nodes in the hidden layer of the LSTM network in extracting layer 1030. A preceding third interim vector (e.g., denoted by u_(p−1)) can have a dimension of H. In some examples, a preceding third interim vector may be a dense vector. Third interim vector 1042 (e.g., denoted by u_(p)) representing the preceding context is an internal representation of preceding context as an output of a current time step of the LSTM network in extracting layer 1030 (e.g., the output values of preceding hidden nodes of the hidden layer of an LSTM network in extracting layer 1030 at a current time step).

In some examples, to generate the third interim vectors, extracting layer 1030 also determines, for a second interim vector 1034 (e.g., denoted by b_(p)), a third interim vector 1044 (e.g., denoted by v_(p)) representing the following context of second interim vector 1034. In some examples, third interim vector 1044 (e.g., denoted by v_(p)) representing the following context can be generated according to formula (9) below.

v _(p) =T{W _(VB) ·b _(p) +W _(VV) ·v _(p+1)}  (9)

where third interim vector 1044 (e.g., denoted by v_(p)) is determined based on a following third interim vector (e.g., denoted by v_(p+1)) representing the following context at a following time step and a second interim vector 1034 (e.g., denoted by b_(p)). In formula (9), b_(p) denotes a second interim vector 1034 at a current time step; v_(p+1) denotes a following third interim vector representing the following context at a following time step; W_(VB) and W_(VV) denote weight matrices of compatible dimensions. In some examples, W_(VB) and W_(VV) can be updated during the training of the LSTM network in extracting layer 1030. In formula (9), T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof.

In some examples, a following third interim vector (e.g., denoted by v_(p+1)) is an internal representation of context from one or more output values of a following time step (e.g., a future time step) associated with hidden nodes in the hidden layer of the LSTM network in extracting layer 1030. A following third interim vector (e.g., denoted by v_(p+1)) can have a dimension of H. In some examples, a following third interim vector may be a dense vector. Third interim vector 1044 (e.g., denoted by v_(p)) representing the following context is an internal representation of following context as an output of a current time step of the LSTM network in extracting layer 1030 (e.g., the output values of following hidden nodes of the hidden layer of an LSTM network in extracting layer 1030 at a current time step).

As illustrated in FIG. 10, similar to those described above, second aggregating layer 1040 can include a forward average pooling stage and a backward average pooling stage, for aggregating the plurality of third interim vectors (e.g., denoted by u₁, u₂ . . . u_(p) and v₁, v₂, . . . v_(p)). In some examples, aggregating the plurality of third interim vectors includes an average pooling, a maximum pooling, or any other pooling algorithms. In the forwarding average pooling stage, one or more subsets of third interim vectors (e.g., u₁, u₂, . . . u_(p), u_(p+1), . . . u_(p)) representing the preceding context are aggregated to generate a fourth interim vector 942 (e.g., denoted by u). In some examples, the forward average pooling stage generates a plurality of fourth interim vectors including fourth interim vector 942 (e.g., denoted by ū) according to formula (10) below.

$\begin{matrix} {\overset{\_}{u} = {\frac{1}{P}{\sum\limits_{p}u_{p}}}} & (10) \end{matrix}$

where, u_(p) denotes the plurality of third interim vectors representing the preceding context; and P represents the number of non-overlapping subsets of [1 . . . K]. Similar to described above, because of the pooling operation in the second aggregating layer 1040, the total number of fourth interim vectors (e.g., vector 942) is less than that of the third interim vectors (e.g., vector 1042). In some examples, fourth interim vector 942 (e.g., denoted by u) has a dimension of H and can be a dense vector.

As described above, second aggregating layer 1040 can also include a backward average pooling stage, for aggregating the plurality of third interim vectors (e.g., denoted by v₁, v₂, . . . v_(p)) representing the following context. In some examples, aggregating the plurality of third interim vectors includes an average pooling, a maximum pooling, or any other pooling algorithms. In some examples, the backward average pooling stage generates a plurality of fourth interim vectors including a fourth interim vector 1054 (e.g., denoted by v) according to formula (11) below.

$\begin{matrix} {\overset{¯}{v} = {\frac{1}{P}{\sum\limits_{p}v_{p}}}} & (11) \end{matrix}$

where, v_(p) denotes the plurality of third interim vectors representing the following context; and P represents the number of non-overlapping subsets of [1 . . . K]. Similar to described above, because of the pooling operation in the second aggregating layer 1040, the total number of fourth interim vectors (e.g., vector 1054) is less than that of third interim vectors (e.g., vector 1044). In the example illustrated in FIG. 10, two fourth interim vectors (vectors 942 and 1054) are shown. It is appreciated, however, that any number of fourth interim vectors may be generated. And as described below, any number of additional stages of extracting and aggregating can be included in encoder 1010. In some examples, fourth interim vector 1054 (e.g., denoted by v) has a dimension of H.

As shown in FIG. 10, in some examples, fourth interim vectors 942 and 1054 are concatenated to generate a vector representing at least a portion of a reconstructed reference set of word (e.g., a vector denoted by z). For example, a single vector can be generated according to z=[ū v]. In some examples, a vector output of encoder 1010 (e.g., vector denoted by z) has dimension 2H. Thus, rather than using a vector of varying dimensions (e.g., due to the varying length of words), a vector output of encoder 1010 provides a fixed-length vector (e.g., having dimension 2H).

While the example provided in FIG. 10 illustrates an extracting and two aggregating stages, it is appreciated that any number of extracting and aggregating stages may be used to generate the desired output vector (e.g., denoted by z). For example, encoder 1010 could include three, four, five, six, etc. extracting and aggregating stages. By increasing the number of stages the encoded context information can be aggregated over increasingly large spans, increasing the effective modeling span of contextual data (e.g., from one or more words in a sentence to the sentence including a plurality of words).

As shown in FIG. 10, the output vector of encoder 1010 (denoted by z) is provided to decoder 1020 for further processing. In some examples, decoder 1020 includes a unidirectional LSTM, which is a type of RNN. A unidirectional LSTM network can also include an input layer, one or more hidden layers, and an output layer. An input layer in the unidirectional LSTM receives a seed vector; the one or more hidden layers provide one or more state vectors (e.g., denoted by g_(k)) for factoring in contextual data; and the output layer generates output vectors using the state vectors. A state vector in a unidirectional LSTM includes a preceding context of a current time step but not a following context of the current time step. In some examples, a preceding context is also referred to as a left context of a current character or word for a current time step.

In some examples, one or more hidden layers of encoder 1010 determine, for a current time step, a state vector (e.g., denoted by g_(k)) representing the preceding context. In some examples, the state vector for a current time step (e.g., denoted by g_(k)) can be generated according to formula (12) below.

g _(k) =T{W _(GZ) ·z+W _(GG) ·g _(k−1)}  (12)

where the state vector g_(k) is generated based on a preceding state vector at a preceding time step (e.g., denoted by g_(k−1)) and a vector output of encoder 1010 (e.g., denoted by z). In formula (12), z denotes the vector output; g_(k−1) denotes the preceding state vector representing the preceding context at a preceding time step; and W_(GZ) and W_(GG) denote weight matrices with compatible dimensions. In some examples, W_(GZ) and W_(GG) can be updated during the training of neural network 810 (e.g., after each feedback received from discriminator 840). In some embodiments, T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof. In some examples, a state vector representing the preceding context at the current time step (e.g., g_(k)) has a dimension of 2H.

A preceding state vector representing the preceding context (e.g., denoted by g_(k−1)) includes an internal representation of context from one or more output values at a preceding time step (e.g., a past time step) in the hidden layer of the LSTM network in decoder 1020. A current stated vector representing the preceding context (e.g., denoted by g_(m)) includes an internal representation of context from one or more output values at a current time step in the hidden layer of the LSTM network in generator 840.

With reference to FIG. 10, decoder 1020 generates, based on the current vector representing the preceding context (e.g., denoted by g_(k)), one or more vectors representing a reconstructed reference set of words (e.g., an output vector 1022 denoted by w_(k′)). In some examples, the reconstructed reference set of words 1022 represents a set of words similar to the reference set of words 804 with some variability introduced to incorporate errors into the reference set of words 804. In some examples, the reconstructed reference set of words 1022 represents a set of words similar to the reference set of words 804 with changes that may not be considered errors. For example, the reconstructed reference set of words 1022 may have the order of words changes, a word substituted, a character substituted, the punctuation substituted, etc. when compared to the reference set of words 804. In some examples, one or more vectors representing a reconstructed reference set of words (e.g., denoted by w_(k′)) can be generated using the formula (13) below.

w _(k′) =S{W _(CG) ·g _(k)}  (13)

where, g_(k) represents a state vector representing the preceding context at a current time step; and W_(CG) represents a weight matrix with compatible dimensions. W_(CG) can be updated during unsupervised training of neural network 810. In some examples, S{ } denotes a softmax activation function. In some examples, one or more vectors representing a word (e.g., denoted by w_(k′)) in the reconstructed reference set of words 1022 has a 1-of-N encoding (e.g., similar to the encoding of characters in the set of input words 802) and thus has a dimension of N. In some examples, one or more vectors representing a word (e.g., denoted by w_(k′)) in the reconstructed reference set of words 1022 are combined to create the reconstructed reference set of words 1022. In some examples. One or more words created by the reconstructor are combined to create the reconstructed reference set of words 1022.

Similar to described above, N represents the total number of distinct words in a predetermined word collection. In some examples, a decoder output vector representing a word (e.g., denoted by w_(k′)) is measured over the span [1 . . . (K+1)], where K is the total number of words in the reconstructed reference set of words. For example, for a reconstructed reference set of words such as “I'm going to the movie too,” K is 7. In some examples, the vectors representing the reconstructed reference set of words includes a vector representing an end-of-sentence symbol </s>, which represents the end of the reconstructed reference set of words 1022. In some examples, the end-of-symbol </s> corresponds to (K+1) word in the reconstructed reference set of words. In some examples, the number K associated with the reconstructed reference set of words does not necessarily equal to the number L associated with the set of input words 802. As described above, the reconstructed reference set of words generated by decoder 1020 represents a set of words similar to the reference set of words 804 with changes that may or may not be considered grammatical errors. Therefore, the total number of words in the reconstructed reference set of words generated by decoder 1020 (e.g., K) can be different from the total number of characters in the input word (e.g., L), depending on the type of change.

As shown in FIG. 10, the output vector of decoder 1020 (e.g., denoted by w_(k′)) representing the reconstructed reference set of words 1022 can then be provided to discriminator 840 for further processing. Returning to FIG. 9, in some examples, discriminator 840 can include a RNN, for example, a bi-direction LSTM. Discriminator 840 determines, for a decoder output vector (e.g., vector 1022 denoted by w_(k′)), a state vector (e.g., denoted by [d_(k) e_(k)]) having a dimension of 2H. In some examples, the state vector can include a first state vector (e.g., denoted by d_(k)) representing the preceding context (also referred to as left context) and a second state vector (e.g., denoted by e_(k)) representing the following context (also referred to as right context). In some examples, a first state vector (e.g., denoted by d_(k)) representing the preceding context can be determined according to formula (14) below.

d _(k) =T{W _(DX) ·w _(k′) +W _(DD) ·d _(k−1)}  (14)

where the first state vector (e.g., denoted by d_(k)) representing the preceding context is generated based on a preceding first state vector representing the preceding context at a previous time step (e.g., denoted by d_(k−1)) and a decoder output vector (e.g., vector 1022 denoted by w_(k′)). In formula (14), w_(k′) denotes a decoder output vector (e.g., vector 1022); d_(k−1) denotes the preceding state vector representing the preceding context; and W_(DX) and W_(DD) denote weight matrices of compatible dimensions. W_(DX) and W_(DD) can be updated during the unsupervised training of neural network 810. In some examples, T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof. In some examples, a first state vector representing the preceding context (e.g., denoted by d_(k)) has a dimension of H.

Similarly to the vectors generated by the other LSTM networks described herein, a preceding first state vector (e.g., denoted by d_(k−1)) representing the preceding context at a previous time step is an internal representation of context from one or more output values of a preceding time step (e.g., a past time step) in the hidden layer of the LSTM network in discriminator 840. Likewise, a first state vector (e.g., denoted by d_(k)) representing the preceding context at a current time step is an internal representation of preceding context from one or more output values of a current time step in the hidden layer of the LSTM network in discriminator 840.

In some examples, discriminator 840 determines, for a decoder output vector (e.g., vector 1022 denoted by w_(k′)), a second state vector (e.g., denoted by e_(k)) representing the following context. In some examples, a second state vector (e.g., denoted by e_(k)) representing the following context can be determined according to formula (15) below.

e _(k) =T{W _(EX) ·w _(k′) +W _(EE) ·e _(k+1)}  (15)

where the second state vector (e.g., denoted by e_(k)) representing the following context is determined based on a following second state vector representing the following context at a following time step (e.g., denoted by e_(k)+1) and the decoder output vector at a current time step (e.g., vector 1022 denoted by w_(k′)). In formula (15), w_(k′) denotes the decoder output vector at a current time step (e.g., vector 1022); e_(k)+1 denotes the following second state vector representing the following context at a following time step (a future time step); and W_(EX) and W_(EE) denote weight matrices of compatible dimensions. W_(EX) and W_(EE) can be updated during the unsupervised training of neural network 810. In some examples, T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof. In some examples, a second state vector representing the following context (e.g., denoted by e_(k)) at a current time step can have a dimension of H.

Similarly to those described above, a following second state vector representing the following context (e.g., denoted by e_(k)+1) is an internal representation of context from one or more output values of a following time step (e.g., a future time step) in the hidden layer of the LSTM network in discriminator 840. Likewise, a second state vector (e.g., denoted by e_(k)) representing the following context is an internal representation of following context from one or more output values of a current time step in the hidden layer of the LSTM network in discriminator 840.

As shown in FIG. 9, in some examples, a vector representing the transformed set of words 822 is also provided to discriminator 840. In some examples, discriminator 840 determines, for a vector representing the transformed set of words (e.g., vector 822 denoted by y_(l′)), a state vector (e.g., denoted by [d_(l) e_(l)]) having a dimension of 2H. In some examples, the state vector can include a first state vector (e.g., denoted by d_(l)) representing the preceding context (also referred to as left context) and a second state vector (e.g., denoted by e_(l)) representing the following context (also referred to as right context). In some examples, a first state vector (e.g., denoted by d_(l)) representing the preceding context can be determined according to formula (16) below.

d _(l) =T{W _(DX) ·y _(l′) +W _(DD) ·d _(l−1)}  (16)

where the first state vector (e.g., denoted by d_(l)) representing the preceding context is generated based on a preceding first state vector representing the preceding context at a previous time step (e.g., denoted by d_(l−1)) and a vector representing the transformed set of words (e.g., vector 822 denoted by y_(l′)). In formula (16), y_(l′) denotes a vector representing the transformed set of words (e.g., vector 822); d_(l−1) denotes the preceding state vector representing the preceding context; and W_(DX) and W_(DD) denote weight matrices of compatible dimensions. W_(DX) and W_(DD) can be updated during the unsupervised training of neural network 810. In some examples, T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof. In some examples, a first state vector representing the preceding context (e.g., denoted by d_(l)) has a dimension of H.

Similarly to the vectors generated by the other LSTM networks described herein, a preceding first state vector (e.g., denoted by d_(l−1)) representing the preceding context at a previous time step is an internal representation of context from one or more output values of a preceding time step (e.g., a past time step) in the hidden layer of the LSTM network in discriminator 840. Likewise, a first state vector (e.g., denoted by d_(l)) representing the preceding context at a current time step is an internal representation of preceding context from one or more output values of a current time step in the hidden layer of the LSTM network in discriminator 840.

In some examples, discriminator 840 determines, for a vector representing the transformed set of words (e.g., vector 822 denoted by y_(l′)), a second state vector (e.g., denoted by e_(l)) representing the following context. In some examples, a second state vector (e.g., denoted by e_(l)) representing the following context can be determined according to formula (17) below.

e _(l) =T{W _(EX) ·y _(l′) +W _(EE) ·e _(l+1)}  (17)

where the second state vector (e.g., denoted by e_(l)) representing the following context is determined based on a following second state vector representing the following context at a following time step (e.g., denoted by e_(l+1)) and the a vector representing the transformed set of words at a current time step (e.g., vector 822 denoted by y_(l′)). In formula (17), y_(l′) denotes the vector representing the transformed set of words at a current time step (e.g., vector 822); e_(l+1) denotes the following second state vector representing the following context at a following time step (a future time step); and W_(EX) and W_(EE) denote weight matrices of compatible dimensions. W_(EX) and W_(EE) can be updated during the unsupervised training of neural network 810. In some examples, T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof. In some examples, a second state vector representing the following context (e.g., denoted by e_(l)) at a current time step can have a dimension of H.

Similarly to those described above, a following second state vector representing the following context (e.g., denoted by e_(l+1)) is an internal representation of context from one or more output values of a following time step (e.g., a future time step) in the hidden layer of the LSTM network in discriminator 840. Likewise, a second state vector (e.g., denoted by e_(l)) representing the following context is an internal representation of following context from one or more output values of a current time step in the hidden layer of the LSTM network in discriminator 840.

In some examples, discriminator 840 then determines a discriminator-output vector 942 (e.g., denoted by q) indicating the probability that the one or more transformed set of words 822 (e.g., represented by the vector denoted by y_(l) . . . y_(l′) . . . y_(L+1)) have a probability distribution corresponding to the probability distribution of the sets of reconstructed reference words 1022 and are thus grammatically correct. In some examples, the one or more sets of reference words 804 can be used to configure the initial state of the discriminator 840 (e.g., configure the initial weights of an LSTM network of discriminator 840). As described above, the one or more sets of reference words 804 includes sets of words that do not have grammatical errors provided by human users or sourced from references like dictionaries, etc. To more accurately train neural network 810 (e.g., the GAN) the one or more sets of reconstructed reference words 1022 may be continually updated to introduce increased variability and therefore many more different grammatical correction situations. Thus, the probability distribution of the sets of reconstructed reference words is determined, for example, at the time each reconstructed reference set of words is generated. During the training of discriminator 840, the probability distribution of the vectors representing the one or more transformed sets of words 822 is also determined and compared to the probability distribution of reconstructed reference words 1022. The result of the comparison is thus the probability that the one or more transformed sets of words 822 (e.g., a theoretical set of words in which all grammatical errors have been corrected) is a grammatically correct version of the one or more sets of input words 802. In some examples, the discriminator-output vector 942 (e.g., denoted by q) is generated according to formula (18) below.

q=S{W _(QD)·[d _(L′+1) e _(l)]}  (18)

where the discriminator-output vector 942 (e.g., q) is generated after all vectors representing the transformed sets of words 822 are processed by the LSTM network in discriminator 840. In some examples, the discriminator-output vector 942 is encoded as a binary vector based on the (L+1)th first state vector (e.g., denoted by d_(L+1)) representing all the preceding context and the 1st second state vectors (e.g., e_(l)) representing all the following context. In formula (18), d_(L+1) denotes the last state vector representing the preceding context; e_(l) denotes the first state vector representing the following context; and W_(QD) denotes a weight matrix of compatible dimensions. W_(QD) can be updated during training of neural network 810. By using d_(L+1), all of the preceding context is encoded into the discriminator-output vector 942 (e.g., q) because d_(L+1), as the last state vector representing the preceding context, considers the context for all preceding time-steps. Similarly, by using e_(l), all of the following context is encoded into the discriminator-output vector 942 (e.g., q) because e_(l), as the first state vector representing following context, considers the context for all following time-steps. In some examples, S{ } denotes a softmax activation function.

As described above, the discriminator-output vector 942 (e.g., q) conveys the probability that the sets of transformed words 822 (e.g., represented by the vector denoted by y_(l) . . . y_(l′) . . . y_(L+1)) have a probability distribution corresponding to the probability distribution of the sets of reconstructed reference words 1022 and are thus grammatically correct. In some examples, the first generator-generated transformed set of words 822, which is generated by generator 820 based on the set of input words 802, may not be considered a grammatically correct. That is, when the first plurality of vectors representing the set of transformed words is provided to discriminator 840, the probability distribution of the generator-generated sets of transformed words 822 is likely dissimilar or does not match the probability distribution of the sets of reconstructed reference words 1022 (e.g., a grammatically correct sentence similar to the reference set of words 804). In other words, the probability indicated discriminator-output vector 942 (e.g., q) is likely low (e.g., near zero).

Accordingly, in some examples, neural network 810 (e.g., the GAN) is trained in an unsupervised manner by allowing the neural network 810 to iteratively generate sets of transformed words 822 and sets of reconstructed reference words 1022 and then determine the discriminator-output vector 942. In each iteration, generator 820 receives feedback (e.g., based on discriminator-output vector 942 denoted by q) and updates at least one of the parameters (e.g., weight matrixes) of generator 820. Similarly, in each iteration, discriminator 840 receives a generator-generated set of transformed words and updates the parameters of the discriminator 840. This iterative, unsupervised training process is continued until the discriminator-output vector 942 approaches a predetermined threshold, indicating that the generated transformed set of words corresponds to a grammatically correct set of words (e.g., a grammatically correct sentence). In some examples, the unsupervised training converges in a linear or nonlinear manner.

In some examples, the unsupervised training of neural network 810 is determined to be complete when a cost function representing the neural network 810 converges. In particular, the GAN iterative framework of neural network 810 can be modeled as a minimax cost function, in which the generator 820 (e.g., denoted by

) and the discriminator 840 (e.g., denoted by D) are jointly trained in an unsupervised manner by jointly solving formula (19) below.

$\begin{matrix} {{\min_{ɛ}{\min_{G}{\max_{D}{K\left( {D,,\mathcal{E}} \right)}}}} = {{E_{W\sim D}\left\{ {\Delta \left\lbrack {W,{\mathcal{E}^{- 1}\left( {\mathcal{E}(W)} \right)}} \right\rbrack} \right\}} + {E_{{G{(W)}}\sim D^{\prime}}\left\{ {\log \left\lbrack {D\left( {(W)} \right)} \right\rbrack} \right\}} + {E_{{G{(X)}}\sim D^{\prime}}\left\{ {\log \left\lbrack {1 - {D\left( {(X)} \right)}} \right\rbrack} \right\}}}} & (19) \end{matrix}$

where K(D,

,ε) denotes the overall cost function, E denotes decoder 1020 that maps z=ε(

(W)≈ε(W) to W′≈W, D′ denotes a probability distribution of the sets of transformed words 822, D denotes the probability distribution of the sets of reconstructed reference words 1022, and Δ[W,ε⁻¹(ε(W))] is a normalized distance metric which is 0 when Y=

(X), that is, the value will be 0 when the sets of transformed words 822 (e.g., Y) are grammatically correct. Formula (19) allows generator 820 to generate sets of words (e.g., sentences) that conform to the general patterns of grammaticality observed in the one or more sets of reference words 804. Thus, the ability of neural network 810 to correct a random input sentence X is increased, even if specific sets of words occurring in X are not incorporated into the sets of reference words 804.

For example, if the set of input words 802 is “I'm going to the party two” and the generator-generated set of transformed words 822 is also “I'm going to the party too,” Δ[W,ε⁻¹(ε(W))]] is 0, indicating that the set of transformed words 822 is grammatically correct. As the one or more sets of reconstructed reference words 1022 and the generator-generated set of transformed words 822 become more dissimilar, the value of Δ[W,ε⁻¹(ε(W))]] will increase, approaching 1 when the generator-generated set of transformed words 822 is most dissimilar and thus is not grammatically correct. For example, if the reconstructed reference set of words is “I'm going to the party too” and the generator-generated set of transformed words 822 is “Im going to the party two,” the value of Δ[W,ε⁻⁴ (ε(W))]] will approach 1.

Thus, the unsupervised training of neural network 810 can be determined to be complete based on two probability distributions. The first probability distribution is associated with the reconstructed reference set of words 1022 (e.g., a grammatically correct sentence) and the second probability distribution is associated with the set of transformed words 822. The unsupervised training is determined to be complete if the difference between an expectation with respect to the first probability distribution and an expectation with respect to the second probability distribution is minimized (e.g., the generator-generated set of transformed words 822 has a similar or same probability distribution as that of the reconstructed reference set of words 1022, which is a grammatically correct sentence).

In some examples, the unsupervised training of neural network 810 is not determined to be complete if the difference between an expectation with respect to the first probability distribution and an expectation with respect to the second probability distribution is not minimized. In some examples, if the unsupervised training of neural network 810 is not determined to be complete if at least one of the parameters of the generator 820 and the parameters of the discriminator 840 can be updated to produce a different result.

In some examples, the training of neural network 810 may further include processing the data in the reverse order discussed above. In some examples, the set of transformed words 822 may be provided to discriminator 840 as an input and discriminator 840 may generate a second set of transformed words based on the set of transformed words 822. Generator 820 may then compare the second set of transformed words to the reconstructed reference set of words 1022 and provide an indication of whether the second set of transformed words includes a grammatical error, similarly to how discriminator 840 provides an indication of whether transformed set of words 822 is grammatically correct, discussed above. This indication may provide further evidence as to whether neural network 810 has been trained correctly. For example, if neural network 810 has completed training a set of words may be provided to either the generator 820 or the discriminator 840 to determine a set of transformed words that is grammatically correct, or a set of transformed words that is grammatically incorrect, respectively.

In some examples, sets of words may be provided to the generator 820 and to the discriminator 840 iteratively, to train neural network 810 to recognize a wide variety of different inputs including grammatical errors and not including grammatical errors. For example, as described above, the set of transformed words 822 may be received from generator 820 and then provided to discriminator 840 as an input to generate a second set of transformed words. The second set of transformed words may be provided to generator 820 as an input to generate a third set of transformed words. After each generation of the sets of transformed words either the generator 820 or the discriminator 840 may make a determination of whether the set of transformed words includes or does not include a grammatical error. This cycling of outputs from each of the generator 820 and discriminator 840 as inputs to the discriminator 820 and the generator 840 may repeated as many times as necessary to complete the training of neural network 810. As described above, cycling the data in this way trains neural network 810 in a more robust manner, providing a wide variety of sets of words for neural network 810 to analyze without requiring a large amount of manual generation from a user. Rather, the data is automatically generated and fed back into the system to provide variations that make neural network 810 more responsive.

Once the unsupervised training of neural network 810 is complete, various portions of neural network 810 can be used to implement a system for correcting user input grammatical errors. In some examples, neural network 810 can determine one or more sets of transformed words 822 that have the same or similar probability to the sets of reconstructed reference words 1022 based on various sets of input words 802. These sets of transformed words 822 represent corrected versions of the sets on input words 802 and can be continually generated for different sets of input words 802 (e.g., sentences) to develop a corpus of sets of words including grammatical errors and the corresponding corrected sets of words. In some examples, this corpus may then be used to train grammatical error model 812 (e.g., a neural network or a classifier) to provide suggested corrected sets of words based on a set of words including a grammatical error.

In some examples, once the unsupervised training of neural network 810 is complete, the generator 820 may be extracted and used independently as grammatical error model 812 to provide grammar error correction suggestions. In particular, after the training of neural network 810 is complete, generator 820 will automatically provide one or more sets of transformed words 822 that do not contain grammatical errors when provided with one or more sets of input words 802 that do contain grammatical errors. Thus, generator 820 may be integrated into another system, such as a digital assistant, to provide automatic grammatical error detection, as well as suggestions for how those grammatical errors may be corrected. In some examples, the suggestions for how to correct grammatical errors may be automatically applied to provide a corrected set of words.

In some examples, on the unsupervised training of neural network 810 is complete, the discriminator 840 may be extracted and used independently to provide one or more sets of words that include a grammatical error after being provided a set of words that does not include a grammatical error. In this way, a large body of randomly generated sets of words that include a grammatical error may be generated quickly. This body of sets of words that include a grammatical error may then be used to help train other neural networks such as grammatical error model 812 to provide grammatical error corrections or perform other types of analysis.

By training the neural network 810 (e.g., the GAN) in an unsupervised manner the various networks that can be used to provide grammatical error correction can be trained quickly and more accurately. In this way, a network for grammatical error correction (e.g., grammatical error model 812) can be generated in an efficient and practical manner for integration into a digital assistant. Thus, the improved grammatical error model may be able to correct a wider range of grammatical errors, including some that the neural network 810 did not encounter during the training, representing an improvement over conventional typographical error models. Further, the improved grammatical error model may provide more accurate detection of grammatical errors and more intelligent corrections to the user, resulting in improved user interaction.

FIG. 11 illustrates a user interface for receiving user input text, according to various examples. As illustrated in FIG. 11, an electronic device 1100 provides a user interface 1102 for receiving and displaying user input including a set of words. For example, a user may enter text (e.g., by using a keyboard or voice input) into user interface 1102 for incorporation into an email, text message, or other communication. In some examples, the user may enter text into user interface 1102 for incorporation into another application to perform a task corresponding to the user input. In one example, the user provides input 1104 including a set of words on user interface 1102, such as the set of words “I'm going to the mall two.” The electronic device 1100 then displays the user input 1104 “I'm going to the mall two” on the user interface 1102. User input 1104 can then be provided to a digital assistant for grammatical error detection and correction.

FIG. 12 illustrates a block diagram of a digital assistant 1200 for providing a corrected set of words, according to various examples. In some examples, digital assistant 1200 (e.g., digital assistant system 700) is implemented by a user device (e.g., user device 1100) according to various examples. In some examples, the user device, a server (e.g., server 108), or a combination thereof, can implement digital assistant 1200. The user device can be implemented using, for example, device 104, 200, 400, 600, 1100, or 1300 as illustrated in FIGS. 1, 2A-2B, 4, 6A-6B, 11, and 13A-13B. In some examples, digital assistant 1200 can be implemented using digital assistant module 726 of digital assistant system 700. Digital assistant 1200 includes one or more modules, models, applications, vocabularies, and user data similar to those of digital assistant module 726. For example, digital assistant 1200 includes the following sub-modules, or a subset or superset thereof: an input/output processing module, an STT process module, a natural language processing module, a task flow processing module, and a speech synthesis module. These modules can also be implemented similar to that of the corresponding modules as illustrated in FIG. 7B, and therefore are not shown and not repeatedly described.

In some examples, digital assistant 1200 includes a neural network 1240 (e.g., grammatical error model 812). In some examples, neural network 1240 can include a portion of neural network 810, discussed above. For example, as described above, after the unsupervised training of neural network 810 is completed, generator 820 can be extracted and implemented in digital assistant 1200 as neural network 1240. In some examples, the training of neural network 810 can be performed at a digital assistant operating on a server. An instance of the trained generator 820 can then be installed or provisioned to a user device (e.g., device 1300) as neural network 1240. In some examples, neural network 1240 can be re-trained or updated as more user input are available.

In some examples, neural network 1240 includes a neural network (e.g., grammatical error model 812) trained with data generated by neural network 810. For example, after the unsupervised training of neural network 810 is completed, sets of corrected words can be determined for corresponding sets of words including grammatical errors. These sets of words can be used as a set of training data to train neural network 1240 to recognize and correct grammatical errors. In some examples, neural network 1240 can be trained with this data in a supervised, unsupervised, or combined manner. In some examples, neural network 1240 is trained on a server. In some examples, neural network 1240 includes a classifier, a recurrent neural network, or any other suitable type of neural network. In some examples, neural network 1240 can be re-trained or updated as more user input are available. In some examples, neural network 1240 is at least partially trained on a user device.

With reference to FIG. 11 and FIG. 12, the user input 1104 can be provided to a digital assistant 1200 as user input 1220. As illustrated by FIG. 12, the user input 1220 is then provided to neural network 1240 for grammatical error detection. In some example, to determine whether the user input 1220 includes a grammatical error, neural network 1240 determines one or more incorrect characters of the user input 1220. For example, if the user input 1220 is “I'm going to the vall too,” neural network 1240 may determine that the “v” in “vall” is incorrect. In some examples, to determine whether the user input 1220 includes a grammatical error, neural network 1240 determines one or more missing characters (e.g., letter, punctuation, etc.) from the user input 1220. For example, if the user input 1220 is “Im going to the mall too,” neural network 1240 may determine that user input 1220 is missing an apostrophe between “I” and “m.”

In some examples, to determine whether the user input 1220 includes a grammatical error, neural network 1240 determines one or more incorrect words of the user input 1220. For example, if the user input 1220 is “I'm going to the mall two,” neural network 1240 may determine that the word “to” in this context is incorrect. In this way neural network 1240 may recognize a variety of different types of grammatical errors, including those that are caused by words that are spelled correctly but used incorrectly, as well as different types of errors caused by incorrect punctuation, misspellings, etc.

In some examples, as discussed above, neural network 1240 determines whether the user input 1220 includes a grammatical error based on the user input 122 and a context of the user input 1220. In some examples, the context of the user input 1220 includes one or more words preceding and/or following a current word of the user input 1220. For example, user input 1220 can include the sentence “I'm going to the mall two.” As discussed above, in this example, neural network 1240 can consider the context of each of the words of input 1220 when determining if user input 1220 includes a grammatical error. Based on the context of the sentences as a whole, neural network 1240 may determine that using the number two is incorrect (e.g., because the sentence is not specifying a number of things). Thus, neural network 1240 may identify that the use of “to” is a grammatical error within user input 1220. In some examples, the context of the user input 1220 includes one or more sets of words (e.g., sentences) preceding and/or following a current sentence of the user input 1220.

As illustrated in FIG. 12, if neural network 1240 detects a grammatical error in user input 1220, neural network 1240 provides one or more candidate words 1260 based on user input 1220 and the grammatical error(s) detected. For example, neural network 1240 can determine that the user input 1220 “There going to the mall later” includes a grammatical error. Neural network 1240 may further determine, based on the unsupervised training of neural network 810, that the type of grammatical error is a homophone error. Based on this determination, neural network 1240 may then determine one or more candidate words 1260 absent grammatical errors based on the provided information. In this example, neural network 1240 may determine that candidate words 1260 include “They're,” or “They are.”

In some examples, neural network 1240 provides a set of candidate words 1260 based on user input 1220 and the grammatical error(s) detected. In some examples, neural network 1240 detects multiple grammatical errors in user input 1220 and provides a set of candidate words 1260 to correct all of the detected grammatical errors. For example, neural network 1240 can determine that the user input 1220 “Im going to the mall two,” includes multiple grammatical errors. Neural network 1240 may further determine that one of the grammatical errors is an apostrophe error and that one of the grammatical errors is a homophone error. Thus, neural network 1240 determines that “Im” in user input 1220 is missing an apostrophe and that “to” is used in the incorrect context. Accordingly, neural network 1240 may provide the set of candidate words (e.g., a sentence) “I'm going to the mall too,” which is absent the grammatical errors in user input 1220.

In some examples, neural network 1240 determines candidate words 1260 based on a context of the user input 1220. In some examples, the context of the user input 1220 can include one or more words preceding or following a current word (e.g., the word being inspected for potential grammatical errors) of the user input 1220.

In some examples, if neural network 1240 does not detect a grammatical error in the user input 1220, it forgoes providing one or more candidate words or sets of candidate words 1260. In some examples, if neural network 1240 does not provide any candidate words, digital assistant 1200 continues to display the user input 1104 in the user interface 1102 without providing any candidate words.

FIGS. 13A and 13B illustrate user interfaces for providing candidate words 1260 absent grammatical errors, according to various examples. With reference to FIG. 13A, candidate words 1260A-B absent grammatical errors are displayed by electronic device 1300 on user interface 1302. The user can then provide a user selection using one or more fingers 302 to select one of candidate words 1260A-B. After the selection of a candidate word is received from the user, the selected candidate word will replace the original user input 1304 in the user interface so that a corrected user input is displayed.

With reference to FIG. 12 and FIG. 13A, in one example, the user provides the user input 1304 “There going to the mall later” to user interface 1302. Digital assistant operating on electronic device 1300 provides the user input 1304 to neural network 1240 which detects that user input 1304 includes the homophone grammatical error of using “to,” based on the context. Based on the detected grammatical error, neural network 1240 can determine candidate words such as “They're” and “They are.” Digital assistant 1200 can display “They're” and “They are” as candidate words 1260 A-B on user interface 1302, respectively. The user may then select one of displayed candidate words 1260A-B to be incorporated into user input 1304, corrected the detected grammatical error. For example, the user can provide a user election to select “They're” (e.g., candidate word 1260A) as the correct candidate word and the displayed user input “There going to the mall later” can be replaced with “They're going to the mall later,” so that the displayed user input is corrected.

In some examples, correcting the displayed user input 1304 includes deleting at least a portion of the displayed user input 1304 and displaying a selected candidate word. In some examples, correcting the displayed user input 1304 includes deleting an incorrect character of displayed user input 1304 so that displayed user input 1304 matches a selected candidate word (e.g., deleting an unnecessary apostrophe). In some examples, correcting the displayed user input 1304 includes adding a character to displayed user input 1304 so that displayed user input 1304 matches a selected candidate word (e.g., adding an apostrophe). In some examples, correcting the displayed user input 1304 includes deleting one or more incorrect characters of displayed user input 1304 and adding one or more characters to displayed user input 1304 so that displayed user input 1304 matches a selected candidate word. In some examples, correcting the displayed user input 1304 includes deleting one or more incorrect words of displayed user input 1304 and adding one or more words to displayed user input 1304 so that displayed user input 1304 matches a selected candidate set of words.

With reference to FIG. 12 and FIG. 13B, in some examples, the user provides the user input 1304 “There going to the mall later” to user interface 1302. The user interface 1302 displays the user input 1304 when the user inputs the text. Digital assistant 1200 operating on device 1300 provides the user input 1304 to neural network 1240, which detects that user input 1304 includes a grammatical error. Neural network 1240 of digital assistant 1200 determines, based on the detected grammatical error, a plurality of candidate words to correct the grammatical error. In some examples, neural network 1240 can then determine a ranking of the plurality of candidate words to correct the grammatical error and provide the highest ranked candidate word 1312 to be displayed on the user interface 1302. The digital assistant 1200 operating on device 1300 can then correct the displayed user input 1304 with the highest ranked candidate word 1312 (e.g., “They're”).

In some examples, the ranking of the plurality of candidate words is based on which candidate word is most frequently selected by users (e.g., the candidate words popularity). In some examples, the ranking of the plurality of candidate words is based on the context associated with the user input 1304. In some examples, the context associated with the user input 1304 includes characters, words, or sentences preceding or following the current word or character of the user input 1304. In some examples, the context associated with the user input 1304 includes the sentence or paragraph structure of the user input 1304 and whether a portion of the user input 1304 is at the beginning or end of a sentence or paragraph.

For example, as illustrated in FIG. 13B, a user provides a user input 1304 of “There going to the mall later.” Neural network 1240 determines that the user input 1304 “There going to the mall later” includes a grammatical error and then determines a plurality of candidate words, such as “They're,” and “They are.” Neural network 1240 can then determine a ranking of They're,” and “They are” based on the popularity of each of each of the candidate words. For example, neural network 1240 may determine that the particular user or a group of users most often select the word “They're,” followed by “They are.” Thus, neural network 1240 may determine that “They're” is the highest ranked candidate word and provide “They're” as candidate word 1312. In another example, the user input 1304 may be “I'm going to the mall two.” In this example, neural network 1240 may evaluate the user input by considering the entire phrase as context, to determine that the highest ranked candidate word is “too.”

In some examples, as shown in FIG. 13B, the digital assistant operating on device 1300 automatically corrects the displayed user input 1304 with the highest ranked candidate word 1312. In some examples, correcting the displayed user input 1304 includes deleting at least a portion of the displayed user input 1304 and displaying the highest ranked candidate word 1312 (e.g., replacing “There” with “They're”). In some examples, correcting the displayed user input 1304 includes deleting an incorrect character of displayed user input 1304 so that displayed user input 1304 matches the highest ranked candidate word 1312. In some examples, correcting the displayed user input 1304 includes adding a character to the displayed user input so that the displayed user input matches the highest ranked candidate word (e.g., adding an apostrophe to “Im”, not shown in FIG. 13B). In some examples, correcting the displayed user input includes deleting one or more incorrect characters of the displayed user input and adding one or more characters to the displayed user input so that the displayed user input matches the highest ranked word. In some examples, correcting the displayed user input includes deleting one or more incorrect words of the displayed user input and adding one or more words to the displayed user input so that the displayed user input matches the highest ranked word.

As previously discussed a network implemented in this manner may be trained in an unsupervised way to provide more intelligent and efficient detection and correction of grammatical errors in some examples. Thus an improved model or network may be created and implemented in a system to correct a wider variety of grammatical errors in a more efficient manner and improve the user experience.

The operations described above with reference to FIG. 8 are optionally implemented by components depicted in FIGS. 1-4, 6A-B, and 7A-C. It would be clear to a person having ordinary skill in the art how other processes are implemented based on the components depicted in FIGS. 1-4, 6A-B, and 7A-C.

FIGS. 14A-14D illustrate an exemplary process 1400 for operating a digital assistant to provide grammatical error detection and correction, according to various examples. Process 1400 is performed, for example, using one or more electronic devices implementing a digital assistant. In some examples, process 1400 is performed using a client-server system (e.g., system 100) and the blocks of process 1400 are divided up in any manner between the server (e.g., DA server 106) and a client device. In other examples, the blocks of process 1400 are divided up between the server and multiple client device (e.g., a mobile phone and a smart watch). Thus, while portion of process 1400 are described herein as being performed by particular device of a client-server system, it will be appreciated that process 1400 is not so limited. In other examples, process 1400 is performed using only a client device (e.g., user device 104) or only multiple client devices. In process 1400, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 1400.

With reference to FIG. 14A, at block 1410, a set of words including a grammatical error (e.g., set of input words 802 as shown in FIG. 8) is received. In some examples, the set of words including a grammatical error includes at least one of a homophone error, an apostrophe error, a subject verb error, and a tense error.

At block 1412, using a neural network based on the set of words including a grammatical error and a reference set of words (e.g., reference set of words 804), a transformed set of words (e.g., transformed set of words 822) is generated. In some examples, the reference set of words comprises a version of the set of words including a grammatical error (e.g., set of input words 802) without any grammatical errors.

At block 1414, based on the set of words including a grammatical error (e.g., set of input words 802) and the reference set of words (e.g., reference set of words 804), a reconstructed reference set of words (e.g., reconstructed reference set of words 1022) is determined.

At block 1416, contextual data of each word of the reference set of words (e.g., reference set of words 804) is aggregated. In some examples, the contextual data comprises words preceding a current word of the reference set of words and following the current word of the reference set of words.

At block 1418, for a current word of the reference set of words (e.g., reference set of words 804), an interim vector (e.g., vector 914) representing the preceding context of the current word is determined. At block 1420, for the current word of the reference set of words, an interim vector (e.g., vector 916) representing the following context of the current word is determined. At block 1422, the interim vectors representing the preceding context for each word of the reference set of words is aggregated (e.g., as vector 1032). At block 1424, the interim vectors representing the following context for each word of the reference set of words is aggregated (e.g., as vector 1034).

At block 1426, a plurality of interim vectors (e.g., vectors 1032 and 1034) based on the results of the aggregating of the contextual data of each word of the reference set of words (e.g., reference set of words 804) is obtained. At block 1428, based on the plurality of interim vectors, a plurality of vectors (e.g., vectors 1042, 1044, 942, and 1054) representing the reconstructed reference set of words are generated.

At block 1430, a plurality of second interim vectors based on the plurality of interim vectors (e.g., vectors 1042 and 1044) are generated using a recurrent neural network layer. At block 1432, the plurality of second interim vectors are aggregated (e.g., as vectors 942 and 1054). At block 1434, a plurality of vectors (e.g., vectors 942 and 1054) representing the reconstructed reference set of words are generated based on the results of the aggregating of the plurality of second interim vectors.

At block 1436, the reconstructed reference set of words (e.g., reconstructed reference set of words 1022) based on the plurality of vectors (e.g., vectors 942 and 1054) representing the reconstructed reference set of words is generated. At block 1438, a first word of the reconstructed reference set of words based on a first vector of the plurality of the plurality of vectors representing the reconstructed reference set of words is generated. At block 1440, a second word of the reconstructed reference set of words based on a second vector of the plurality of vectors representing the reconstructed reference set of words is generated. At block 1442, the first word and the second word are combined to create the reconstructed reference set of words.

At block 1444, whether the transformed set of words (e.g., transformed set of words 822) is grammatically correct is determined based on a comparison of the transformed set of words and the reconstructed reference set of words (e.g., reconstructed reference set of words 1022). At block 1446, a first probability distribution associated with the transformed set of words is determined. At block 1448, a second probability distribution associated with the reconstructed reference set of words is determined. At block 1450, whether the transformed set of words is substantially similar to the reconstructed reference set of words is determined based on the first probability distribution and the second probability distribution.

At block 1452, an indication of whether the transformed set of words (e.g., transformed set of words 822) is grammatically correct is provided to the neural network (e.g., generator 820). At block 1454, in accordance with an indication that the transformed set of words is grammatically correct the transformed set of words is provided as an output. In some examples, the output transformed set of words is one of a plurality of output transformed sets of words.

At block 1456, in accordance with an indication that the transformed set of words (e.g., transformed set of words 822) is not grammatically correct, a second transformed set of words (e.g., transformed set of words 822) is generated using the neural network (e.g., generator 820) and based on the set of words including a grammatical error (e.g., set of input words 802) and the reference set of words (e.g., reference set of words 804). At block 1458, a second reconstructed reference set of words (e.g., reconstructed reference set of words 1022) is determined based on the set of words including a grammatical error and the reference set of words. At block 1460, whether the second transformed set of words is grammatically correct is determined based on a comparison of the second transformed set of words and the second reconstructed reference set of words. At block 1462, an indication of whether the second transformed set of words is grammatically correct is provided to the neural network (e.g., generator 820).

At block 1464, whether the generator (e.g., generator 820) has been trained based on the indication of whether the transformed set of words is grammatically correct is determined, wherein the trained generator (e.g., generator 8120, grammatical error model 812, neural network 1240) provides one or more grammar error correction suggestions (e.g., candidate words 1260A-B and candidate words 1312) via a user device (e.g., user device 1100 and 1300). At block 1466, whether the discriminator (e.g., discriminator 840) has been trained based on the indication of whether the transformed set of words is grammatically correct is determined, wherein the trained discriminator provides one or more grammatically incorrect set of words.

At block 1468, a neural network (e.g., grammatical error network 812, neural network 1240) is trained with the plurality of output transformed set of words. At block 1470, an input set of words (e.g., user input 1304) is received including at least one grammatical error. At block 1472, one or more grammar correction suggestions (e.g., candidate words 1260A-B and candidate words 1312) for the input set of words is determined.

In accordance with some implementations, a computer-readable storage medium (e.g., a non-transitory computer readable storage medium) is provided, the computer-readable storage medium storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises means for performing any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises a processing unit configured to perform any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises one or more processors and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods or processes described herein.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the techniques and their practical applications. Others skilled in the art are thereby enabled to best utilize the techniques and various embodiments with various modifications as are suited to the particular use contemplated.

Although the disclosure and examples have been fully described with reference to the accompanying drawings, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims.

As described above, one aspect of the present technology is the gathering and use of data available from various sources to improve the detection and correction of grammatical errors in user inputs. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, twitter IDs, home addresses, data or records relating to a user's health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, or any other identifying or personal information.

The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to train models to detect and correct grammatical errors. Accordingly, use of such personal information data enables more accurate detection of errors based on interaction with the user. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. For instance, health and fitness data may be used to provide insights into a user's general wellness, or may be used as positive feedback to individuals using technology to pursue wellness goals.

The present disclosure contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. Such policies should be easily accessible by users, and should be updated as the collection and/or use of data changes. Personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection/sharing should occur after receiving the informed consent of the users. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations. For instance, in the US, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. Hence different privacy practices should be maintained for different personal data types in each country.

Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of grammatical error detection, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services or anytime thereafter. In another example, users can select not to provide user input data to grammatical error detection services. In yet another example, users can select to limit the length of time user input data is maintained. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app.

Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user's privacy. De-identification may be facilitated, when appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of data stored (e.g., collecting location data at a city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods.

Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, grammatical error detection and correction may be implemented based on non-personal information data or a bare minimum amount of personal information, such as a portion of an input associated with a user, other non-personal information available to the grammatical error detection services, or publicly available information. 

What is claimed is:
 1. A non-transitory computer-readable storage medium storing one or more programs for providing grammatical error correction, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: receive a set of words including a grammatical error; generate, using a neural network based on the set of words including a grammatical error and a reference set of words, a transformed set of words; determine, based on the set of words including a grammatical error and the reference set of words, a reconstructed reference set of words; determine, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct; and provide an indication of whether the transformed set of words is grammatically correct to the neural network.
 2. The non-transitory computer-readable storage medium of claim 1, wherein set of words including a grammatical error includes at least one of a homophone error, an apostrophe error, a subject verb error, and a tense error.
 3. The non-transitory computer-readable storage medium of claim 1, wherein the reference set of words comprises a version of the set of words including a grammatical error without any grammatical errors.
 4. The non-transitory computer-readable storage medium of claim 1, wherein determining, based on the set of words including a grammatical error and a reference set of words set of words, a reconstructed reference set of words further comprises: aggregating contextual data of each word of the reference set of words; obtaining a plurality of interim vectors based on the results of the aggregating of the contextual data of each word of the reference set of words; generating, based on the plurality of interim vectors, a plurality of vectors representing the reconstructed reference set of words; and generating the reconstructed reference set of words based on the plurality of vectors representing the reconstructed reference set of words.
 5. The non-transitory computer-readable storage medium of claim 4, wherein aggregating contextual data of each word of the reference set of words further comprises: determining, for a current word of the reference set of words, an interim vector representing the preceding context of the current word; determining, for the current word of the reference set of words, an interim vector representing the following context of the current word; aggregating the interim vectors representing the preceding context for each word of the reference set of words; and aggregating the interim vectors representing the following context for each word of the reference set of words.
 6. The non-transitory computer-readable storage medium of claim 4, wherein generating, based on the plurality of interim vectors, a plurality of vectors representing the reconstructed reference set of words further comprises: generating, using a recurrent neural network layer, a plurality of second interim vectors based on the plurality of interim vectors; aggregating the plurality of second interim vectors; and generating, based on the results of the aggregating of the plurality of second interim vectors, the plurality of vectors representing the reconstructed reference set of words.
 7. The non-transitory computer-readable storage medium of claim 4, wherein the contextual data comprises words preceding a current word of the reference set of words and following the current word of the reference set of words.
 8. The non-transitory computer-readable storage medium of claim 4, wherein generating the reconstructed reference set of words based on the plurality of vectors representing the reconstructed reference set of words further comprises: generating a first word of the reconstructed reference set of words based on a first vector of the plurality of vectors representing the reconstructed reference set of words; generating a second word of the reconstructed reference set of words based on a second vector of the plurality of vectors representing the reconstructed reference set of words; and combining the first word and the second word to create the reconstructed reference set of words.
 9. The non-transitory computer-readable storage medium of claim 1, wherein the neural network includes a generator of a generative adversarial network, and wherein determining, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct, is performed by a discriminator of the generative adversarial network.
 10. The non-transitory computer-readable storage medium of claim 1, wherein determining, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct further comprises: determining a first probability distribution associated with the transformed set of words; determining a second probability distribution associated with the reconstructed reference set of words; determining, based on the first probability distribution and the second probability distribution, whether the transformed set of words is substantially similar to the reconstructed reference set of words.
 11. The non-transitory computer-readable storage medium of claim 1, the one or more programs further comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: in accordance with an indication that the transformed set of words is grammatically correct, provide the transformed set of words as an output.
 12. The non-transitory computer-readable storage medium of claim 1, the one or more programs further comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: in accordance with an indication that the transformed set of words is not grammatically correct: generate, using the neural network based on the set of words including a grammatical error and the reference set of words, a second transformed set of words; determine, based on the set of words including a grammatical error and the reference set of words, a second reconstructed reference set of words; determine, based on a comparison of the second transformed set of words and the second reconstructed reference set of words, whether the second transformed set of words is grammatically correct; and provide an indication of whether the second transformed set of words is grammatically correct to the neural network.
 13. The non-transitory computer-readable storage medium of claim 9, the one or more programs further comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: determine, based on the indication of whether the transformed set of words is grammatically correct, whether the generator has been trained, wherein the trained generator provides one or more grammar error correction suggestions via a user device.
 14. The non-transitory computer-readable storage medium of claim 9, the one or more programs further comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: determine, based on the indication of whether the transformed set of words is grammatically correct, whether the discriminator has been trained, wherein the trained discriminator provides one or more grammatically incorrect sets of words.
 15. The non-transitory computer-readable storage medium of claim 11, wherein the output transformed set of words is one of a plurality of output transformed sets of words.
 16. The non-transitory computer-readable storage medium of claim 15, the one or more programs further comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: train a neural network with the plurality of output transformed sets of words; receive an input set of words including at least one grammatical error; and determine, with the trained neural network, one or more grammar error correction suggestions for the input set of words.
 17. An electronic device, comprising: one or more processors; memory; and one or more programs stored in memory, the one or more programs including instructions for: receiving a grammatically incorrect set of words; generating, using a neural network based on the grammatically incorrect set of words and a reference set of words, a transformed set of words; determining, based on the transformed set of words, a reconstructed reference set of words; determining, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct; and providing an indication of whether the transformed set of words is grammatically correct to the neural network.
 18. A method for providing grammatical error correction, comprising: at one or more electronic devices with one or more processors and memory: receiving a set of words including a grammatical error; generating, using a neural network based on the set of words including a grammatical error and a reference set of words, a transformed set of words; determining, based on the set of words including a grammatical error and the reference set of words, a reconstructed reference set of words; determining, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct; and providing an indication of whether the transformed set of words is grammatically correct to the neural network.
 19. A non-transitory computer-readable storage medium storing one or more programs for providing grammatical error correction, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: receive an input set of words; display the input set of words; determine, using a neural network, whether the input set of words includes at least one grammatical error, wherein the neural network is trained in an unsupervised manner based on input sets of words including grammatical errors and a reference set of words; and in accordance with a determination that the user input includes at least one grammatical error, correct the displayed input set of words. 